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Pipeline run

d865013f-3eab-45e7-8cf5-c743b92d0c2c

Pipeline LLM cost (USD)
API 1: $0.0129 API 2: $0.0006 API 3: $0.0000 Total: $0.0135

Client output enrichment

v2 Skill cluster · Nature of work · AI index · Tech stack maturity · Evidence · KRA description
Nature of work · Data Engineering / Data Warehousing
Build and tune large-scale data warehouse and ETL/ELT pipelines on AWS or Azure, writing Spark/MapReduce and Python/Java code, working heavily with SQL, data modeling, and cloud stack services like S3, Redshift, Glue, EMR, Databricks, and ADF/ADLS.
""Demonstrated strength in architecting data warehouse solutions and integrating technical components.""
Tech stack maturity
Mainstream Modern
The skill set centers on widely adopted cloud platforms and data engineering technologies like AWS, Azure, Spark, Java, Python, and SQL, which aligns with mainstream modern enterprise architecture rather than legacy or bleeding-edge stacks.
AI index (0 = no AI use, 5 = totally AI-dependent · v2.1)
0.20 / 5
· Title match
Has AI skill
· AI skill (primary)
· AI skill (secondary)
· On AI team
· Builds AI products
vocab breakdown (legacy)
Assistants (×1):
Frameworks (×2):
Models / concepts (×3): embeddings
Evidence — skills matched in JD (37)
AWS MapReduce Spark SQL ETL ELT Data Warehousing Data Modeling Python Java Microsoft Azure Kafka Flume Redshift Kinesis S3 EC2 Glue EMR DynamoDB Lambda QuickSight Git Jenkins Apache Subversion +12
Skill cluster (7 dimension groups, role-scoped)
Cloud Platforms
Microsoft Azure S3
Asynchronous Messaging and Event Streaming
Kafka
C# and .NET Languages
C#
Cloud Provider Platforms
AWS
Java Language and JVM
Java
Python Programming
Python
Cross-cutting / unaligned
MapReduce Spark SQL ETL ELT Data Warehousing Data Modeling Flume Redshift Kinesis EC2 Glue EMR DynamoDB Lambda QuickSight Git Jenkins Apache Subversion Hadoop Hive Azure Stack Databricks ADF ADLS .NET StreamSets Snowflake Tableau Alteryx
Show KRA description ↓
AWS Experience with Kafka, Flume and AWS tool stack such as Redshift and Kinesis are preferred. Experience building on AWS using S3, EC2, Redshift, Glue, EMR, DynamoDB, Lambda, QuickSight, etc. Experience using software version control tools (Git, Jenkins, Apache Subversion). AWS certifications or other related professional technical certifications. Experience with cloud or on-premise middleware and other enterprise integration technologies. Experience in writing MapReduce and/or Spark jobs. Demonstrated strength in architecting data warehouse solutions and integrating technical components. Good analytical skills with excellent knowledge of SQL. 4+ years of work experience with very large data warehousing environment. Excellent communication skills, both written and verbal. 7+ years of experience with detailed knowledge of data warehouse technical architectures, infrastructure components, ETL/ ELT and reporting/analytic tools. 4+ years of experience data modelling concepts. 3+ years of Python and/or Java development experience. 3+ years’ experience in Big Data stack environments (EMR, Hadoop, MapReduce, Hive). Azure In-depth cloud domain experience with focus on Microsoft Azure / Microsoft Azure Stack solutions. Experience in writing MapReduce and/or Spark jobs. Experience building on Azure using Databricks, ADF/ADLS. Sound programming, debugging/troubleshooting and problem-solving skills with familiarity in C# / .NET. Good to have experience in Streamsets, Snowflake, Kafka. Flexible and proactive/self-motivated working style with strong personal ownership of problem resolution. Excellent communicator (written and verbal formal and informal). Ability to multi-task under pressure and work independently with minimal supervision. Strong verbal and written communication skills. Must be a team player and enjoy working in a cooperative and collaborative team environment. Adaptable to new technologies and standards. Use an issue-based approach to deliver growth, market and portfolio strategy engagements for corporates. Strong communication, presentation and team building skills and experience in producing high quality reports, papers, and presentations. Experience in executing and managing research and analysis of companies and markets, preferably from a commercial due diligence standpoint. Exposure to tools like Tableau, Alteryx etc. BE/BTech/MCA/MBA Minimum 7 years hand-on experience in one or more key areas. Minimum 10 years industry experience. A Team of people with commercial acumen, technical experience and enthusiasm to learn new things in this fast-moving environment. An opportunity to be a part of market-leading, multi-disciplinary team of 1400 + professionals, in the only integrated global transaction business worldwide. Opportunities to work with EY Consulting practices globally with leading businesses across a range of industries.

Signals

Skill data-engineer
0.55
Alias cloud-architect
1.00
KRA data-engineer
0.57

Post-classification

Centroidupdated · n=16
Alias collision log
New-role queue
New skills captured19
New KRA captured

Captured for admin review

Flume Cloud Architect pending
Kinesis Cloud Architect pending
EC2 Cloud Architect pending
Glue Cloud Architect pending
EMR Cloud Architect pending
DynamoDB Cloud Architect pending
Lambda Cloud Architect pending
QuickSight Cloud Architect pending
Apache Subversion Cloud Architect pending
MapReduce primary Cloud Architect pending
ETL primary Cloud Architect pending
ELT primary Cloud Architect pending
Data Warehousing primary Cloud Architect pending
Data Modeling primary Cloud Architect pending
Azure Stack Cloud Architect pending
ADF Cloud Architect pending
ADLS Cloud Architect pending
StreamSets Cloud Architect pending
Alteryx Cloud Architect pending
Status: completed Created: 2026-05-27T14:40:45.995879Z Updated: 2026-06-12T17:25:49.588325Z API 3 duration: 48327 ms
Flow Current 3-step pipeline

1 POST /skills/extract-from-jd

2 POST /skills/extract-details

3 POST /skills/final-role-output

Role Chosen role & resolution

Cloud Architect

Multi-Cloud / Hybrid Architect

sub-role · 0.87 domain · Cloud Engineering CASE DOMAIN

slug: cloud-architect · id: 9 · source: db · sub-role slug: multicloud-architect

Domain=Cloud Engineering → sub-role multicloud-architect; The JD is centered on architecting data warehouse and integration solutions across AWS and Azure, which best matches a cloud architecture role rather than a single-platform admin or developer role.

Matched skills

AWSKafkaFlumeRedshiftKinesisS3EC2GlueEMRDynamoDBLambdaQuickSightGitJenkinsApache Subversion

Matched dimensions

Cloud ArchitectureData Warehouse ArchitectureBig Data EngineeringETL/ELT and Reporting AnalyticsAzure Cloud SolutionsEnterprise IntegrationData ModelingProgramming and Troubleshooting

Matched KRAs

Experience building on AWS using S3, EC2, Redshift, Glue, EMRExperience building on Azure using Databricks, ADF/ADLSExperience in writing MapReduce and/or Spark jobsDemonstrated strength in architecting data warehouse solutionsIntegrating technical componentsStrong communication, presentation and team building skillsExperience in executing and managing research and analysis

Resolution: in_db — role exists in library; skill↔dim and role↔dim links saved when applicable.

0
New skills
0
Skill↔dim saved
0
Role↔dim saved
3
Skipped

Job description

At EY, you will have the chance to build a career as unique as you are, with the global scale, support, inclusive culture and technology to become the best version of you. And we are counting on your unique voice and perspective to help EY become even better too. Join us and build an exceptional experience for yourself, and a better working world for all.

EY-Consulting - Data and Analytics – Senior – AWS/Azure

EY's Consulting Services is a unique, industry-focused business unit that provides a broad range of integrated services that leverage deep industry experience with strong functional and technical capabilities and product knowledge. EY’s financial services practice provides integrated Consulting services to financial institutions and other capital markets participants, including commercial banks, retail banks, investment banks, broker-dealers & asset management firms, and insurance firms from leading Fortune 500 Companies. Within EY’s Consulting Practice, Data and Analytics team solves big, complex issues and capitalize on opportunities to deliver better working outcomes that help expand and safeguard the businesses, now and in the future. This way we help create a compelling business case for embedding the right analytical practice at the heart of client’s decision-making.

The opportunity

We’re looking for Senior – Cloud Experts with design experience in Bigdata cloud implementations.

Your Key Responsibilities

AWS
Experience with Kafka, Flume and AWS tool stack such as Redshift and Kinesis are preferred.Experience building on AWS using S3, EC2, Redshift, Glue,EMR, DynamoDB, Lambda, QuickSight, etc.Experience using software version control tools (Git, Jenkins, Apache Subversion)AWS certifications or other related professional technical certificationsExperience with cloud or on-premise middleware and other enterprise integration technologiesExperience in writing MapReduce and/or Spark jobsDemonstrated strength in architecting data warehouse solutions and integrating technical componentsGood analytical skills with excellent knowledge of SQL.4+ years of work experience with very large data warehousing environmentExcellent communication skills, both written and verbal7+ years of experience with detailed knowledge of data warehouse technical architectures, infrastructure components, ETL/ ELT and reporting/analytic tools.4+ years of experience data modelling concepts3+ years of Python and/or Java development experience3+ years’ experience in Big Data stack environments (EMR, Hadoop, MapReduce, Hive)

Azure
In-depth cloud domain experience with focus on Microsoft Azure / Microsoft Azure Stack solutionsExperience in writing MapReduce and/or Spark jobsExperience building on Azure using Databricks, ADF/ADLSSound programming, debugging/troubleshooting and problem-solving skills with familiarity in C# / .NETGood to have experience in Streamsets, Snowflake, Kafka.Flexible and proactive/self-motivated working style with strong personal ownership of problem resolution.Excellent communicator (written and verbal formal and informal).Ability to multi-task under pressure and work independently with minimal supervision.Strong verbal and written communication skills.Must be a team player and enjoy working in a cooperative and collaborative team environment.Adaptable to new technologies and standards.

Skills And Attributes For Success
Use an issue-based approach to deliver growth, market and portfolio strategy engagements for corporatesStrong communication, presentation and team building skills and experience in producing high quality reports, papers, and presentations.Experience in executing and managing research and analysis of companies and markets, preferably from a commercial due diligence standpoint.Exposure to tools like Tableau, Alteryx etc.

To qualify for the role, you must have
BE/BTech/MCA/MBAMinimum 7 years hand-on experience in one or more key areas.Minimum 10 years industry experience

What We Look For
A Team of people with commercial acumen, technical experience and enthusiasm to learn new things in this fast-moving environmentAn opportunity to be a part of market-leading, multi-disciplinary team of 1400 + professionals, in the only integrated global transaction business worldwide.Opportunities to work with EY Consulting practices globally with leading businesses across a range of industries

What Working At EY Offers

At EY, we’re dedicated to helping our clients, from start–ups to Fortune 500 companies — and the work we do with them is as varied as they are.

You get to work with inspiring and meaningful projects. Our focus is education and coaching alongside practical experience to ensure your personal development. We value our employees and you will be able to control your own development with an individual progression plan. You will quickly grow into a responsible role with challenging and stimulating assignments. Moreover, you will be part of an interdisciplinary environment that emphasizes high quality and knowledge exchange. Plus, we offer:
Support, coaching and feedback from some of the most engaging colleagues aroundOpportunities to develop new skills and progress your careerThe freedom and flexibility to handle your role in a way that’s right for you

EY | Building a better working world 

EY exists to build a better working world, helping to create long-term value for clients, people, and society and build trust in the capital markets.

Enabled by data and technology, diverse EY teams in over 150 countries provide trust through assurance and help clients grow, transform, and operate.

Working across assurance, consulting, law, strategy, tax, and transactions, EY teams ask better questions to find new answers for the complex issues facing our world today.

Skills from this JD

Each row merges API 1 extraction, API 2 library match / v3 orchestration (dimensions + locked dims), and API 3 persistence tags.

AWS Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: AWS id=187 · aws

Aliases — catalog

  • AWS (CANONICAL) primary

Context tags (catalog)

API Gateway AWS CLI Auto Scaling CloudFormation CloudFront CloudTrail CloudWatch Cognito DynamoDB EC2 ECS EKS Elastic Beanstalk Elastic Load Balancing IAM KMS Lambda RDS Route 53 S3 SNS SQS Serverless VPC

Stored enrichment (catalog DB)

Category
Platform
Sub-category
Cloud Platform
Vendor
Amazon
License
other_open
Year introduced
2006
Confidence
0.99
Version strategy
NOT_APPLICABLE

Maturity reasoning: AWS is a hiring-pipeline staple: it appears in a large share of cloud/DevOps job descriptions and dominates public cloud market share, with broad certification and vendor ecosystem support.

Skill profile (library / DB)

Skill nature
PLATFORM
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
9
Sub-category id
46
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • Cloud Platforms Catalog dimension db id 20

    Library dimension (catalog)

    Roles linked in library: .NET Backend Developer, Backend Developer, Cyber Security Engineer, Data Engineer, DevOps Engineer, Fullstack Developer, Go Backend Developer, Java Backend Developer, Kotlin Backend Developer, ML Engineer, MLOps Engineer, Node.js Backend Developer, Python Backend Developer, Scala Backend Developer

  • Cloud Platforms for AI Deployment Catalog dimension db id 211

    Library dimension (catalog)

    Roles linked in library: AI Engineer

  • Cloud Provider Platforms Catalog dimension db id 131

    Library dimension (catalog)

    Roles linked in library: Cloud Architect, Cloud Security Engineer

  • Cloud Security Posture Tools Catalog dimension db id 64

    Library dimension (catalog)

    Roles linked in library: Cloud Security Engineer, Cyber Security Engineer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Cloud Platforms
cloud-platforms
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Cloud Platforms for AI Deployment
cloud-platforms-for-ai-deployment
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Cloud Provider Platforms
cloud-provider-platforms
Existing dimension (library) · Role↔dimension saved
Cloud Security Posture Tools
cloud-security-posture-tools
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Kafka Secondary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Kafka id=36 · kafka

Aliases — catalog

  • Kafka (CANONICAL) primary

Context tags (catalog)

Apache Flink Apache Kafka Apache Pulsar Apache Spark Avro KSQL Kafka API Kafka Connect Kafka Streams ZooKeeper Zookeeper backpressure brokers consumer consumer group consumer groups event sourcing event-driven architecture exactly-once semantics fault tolerance high throughput log compaction message broker message queue microservices offsets partition partitioning partitions producer producer API real-time analytics real-time data replication schema registry stream processing topic topic partitioning topics

Stored enrichment (catalog DB)

Category
Datastore
Sub-category
Event Stream Store
Vendor
Confluent
License
apache_2
Year introduced
2011
Confidence
0.90
Version strategy
NOT_APPLICABLE

Maturity reasoning: Kafka appears in many production JDs for event streaming and data pipelines, and remains a standard platform in cloud/vendor offerings (e.g., Confluent, AWS MSK), indicating broad hiring demand.

Skill profile (library / DB)

Skill nature
TOOL
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
3
Sub-category id
3533
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • Asynchronous Messaging and Event Streaming Catalog dimension db id 297

    Library dimension (catalog)

    Roles linked in library: .NET Backend Developer, Go Backend Developer, Kotlin Backend Developer, Node.js Backend Developer, Scala Backend Developer

  • Messaging and Background Jobs Catalog dimension db id 291

    Library dimension (catalog)

    Roles linked in library: PHP Backend Developer, Python Backend Developer, Ruby Backend Developer

  • Messaging and Event Streaming Catalog dimension db id 8

    Library dimension (catalog)

    Roles linked in library: Backend Developer, Data Engineer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Asynchronous Messaging and Event Streaming
asynchronous-messaging-and-event-streaming
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Messaging and Background Jobs
messaging-and-background-jobs
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Messaging and Event Streaming
messaging-and-event-streaming
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Flume Secondary New / orchestrated API 3: new canonical path (new) New / unmatched skill (orchestrated in API 2)

Skill enrichment (orchestrator / LLM)

No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).

Derived legacy fields
Category
Data Engineering Tools
Sub-category
general
Skill nature
TOOL
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Redshift Secondary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Redshift id=1210 · redshift

Aliases — catalog

  • Redshift (CANONICAL)

Context tags (catalog)

AWS Amazon S3 ETL Redshift Spectrum SQL analytics business intelligence cluster management columnar storage data lake data migration data modeling data warehousing performance tuning query optimization scalability

Stored enrichment (catalog DB)

Category
Platform
Sub-category
Data Warehouse Platform
Vendor
Amazon
License
proprietary
Year introduced
2012
Confidence
0.93
Version strategy
NOT_APPLICABLE

Maturity reasoning: Amazon Redshift is widely listed in data-warehouse/cloud analytics job descriptions and remains an AWS flagship service; no vendor sunset, and it’s commonly paired with Snowflake/BigQuery rather than replaced.

Skill profile (library / DB)

Skill nature
PLATFORM
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
9
Sub-category id
917
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • Cloud Platforms Catalog dimension db id 20

    Library dimension (catalog)

    Roles linked in library: .NET Backend Developer, Backend Developer, Cyber Security Engineer, Data Engineer, DevOps Engineer, Fullstack Developer, Go Backend Developer, Java Backend Developer, Kotlin Backend Developer, ML Engineer, MLOps Engineer, Node.js Backend Developer, Python Backend Developer, Scala Backend Developer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Cloud Platforms
cloud-platforms
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Kinesis Secondary New / orchestrated API 3: new canonical path (new) New / unmatched skill (orchestrated in API 2)

Skill enrichment (orchestrator / LLM)

No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).

Derived legacy fields
Category
Data Engineering Tools
Sub-category
general
Skill nature
PLATFORM
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
S3 Secondary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: S3 id=1212 · s3

Aliases — catalog

  • S3 (CANONICAL)

Context tags (catalog)

AWS AWS CLI CORS CloudFormation IAM policies S3 Select bucket data lifecycle encryption event notifications multipart upload object storage static website hosting transfer acceleration versioning

Stored enrichment (catalog DB)

Category
Platform
Sub-category
Cloud Storage Platform
Vendor
Amazon
License
proprietary
Year introduced
2006
Confidence
0.90
Version strategy
NOT_APPLICABLE

Maturity reasoning: Amazon S3 is a default cloud storage service in AWS job descriptions and architecture docs; it remains broadly adopted with no vendor sunset, and is commonly paired with S3-compatible storage rather than replaced.

Skill profile (library / DB)

Skill nature
PLATFORM
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
9
Sub-category id
919
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • Cloud Platforms Catalog dimension db id 20

    Library dimension (catalog)

    Roles linked in library: .NET Backend Developer, Backend Developer, Cyber Security Engineer, Data Engineer, DevOps Engineer, Fullstack Developer, Go Backend Developer, Java Backend Developer, Kotlin Backend Developer, ML Engineer, MLOps Engineer, Node.js Backend Developer, Python Backend Developer, Scala Backend Developer

  • React Frontend Development Catalog dimension db id 96

    Library dimension (catalog)

  • Systems Programming Catalog dimension db id 166

    Library dimension (catalog)

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Cloud Platforms
cloud-platforms
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
React Frontend Development
d_init_01
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Systems Programming
d_init_02
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
EC2 Secondary New / orchestrated API 3: new canonical path (new) New / unmatched skill (orchestrated in API 2)

Skill enrichment (orchestrator / LLM)

No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).

Derived legacy fields
Category
Cloud Platforms
Sub-category
general
Skill nature
PLATFORM
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Glue Secondary New / orchestrated API 3: new canonical path (new) New / unmatched skill (orchestrated in API 2)

Skill enrichment (orchestrator / LLM)

No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).

Derived legacy fields
Category
Data Engineering Tools
Sub-category
general
Skill nature
PLATFORM
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
EMR Secondary New / orchestrated API 3: new canonical path (new) New / unmatched skill (orchestrated in API 2)

Skill enrichment (orchestrator / LLM)

No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).

Derived legacy fields
Category
Data Engineering Tools
Sub-category
general
Skill nature
PLATFORM
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
DynamoDB Secondary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Amazon DynamoDB id=93 · amazon-dynamodb

Aliases — catalog

  • Amazon DynamoDB (CANONICAL) primary

Context tags (catalog)

AWS SDK DAX GSI LSI NoSQL Streams TTL boto3 conditional writes on-demand capacity partition key provisioned throughput secondary index sort key transactions

Stored enrichment (catalog DB)

Category
Service
Sub-category
Managed Nosql Database Service
Vendor
Amazon Web Services
License
proprietary
Year introduced
2012
Confidence
0.98
Version strategy
NOT_APPLICABLE

Maturity reasoning: Commonly listed in cloud/backend job descriptions and widely used on AWS; strong vendor adoption and active ecosystem signal broad market demand.

Skill profile (library / DB)

Skill nature
CLOUD_SERVICE
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
11
Sub-category id
55
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • NoSQL Databases Catalog dimension db id 19

    Library dimension (catalog)

    Roles linked in library: Backend Developer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
NoSQL Databases
nosql-databases
Skipped — no persistable v3 meta for new skill
skill_not_in_db_v3_proposed
Lambda Secondary New / orchestrated API 3: new canonical path (new) New / unmatched skill (orchestrated in API 2)

Skill enrichment (orchestrator / LLM)

No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).

Derived legacy fields
Category
Cloud Platforms
Sub-category
general
Skill nature
PLATFORM
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
QuickSight Secondary New / orchestrated API 3: new canonical path (new) New / unmatched skill (orchestrated in API 2)

Skill enrichment (orchestrator / LLM)

No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).

Derived legacy fields
Category
Data Engineering Tools
Sub-category
general
Skill nature
PLATFORM
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Git Secondary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Git id=1002 · git

Aliases — catalog

  • Git (CANONICAL)

Context tags (catalog)

CI/CD GitHub GitLab branching checkout clone commit fork merging pull request rebase remote repository stash versioning

Stored enrichment (catalog DB)

Category
Tool
Sub-category
Version Control Tool
Vendor
Linus Torvalds
License
gpl_v2
Year introduced
2005
Confidence
0.99
Version strategy
NOT_APPLICABLE

Maturity reasoning: Git is a hiring-pipeline staple: it appears in the vast majority of software engineering job descriptions and is the default VCS on GitHub/GitLab/Bitbucket.

Skill profile (library / DB)

Skill nature
TOOL
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
13
Sub-category id
730
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • React Frontend Development Catalog dimension db id 96

    Library dimension (catalog)

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
React Frontend Development
d_init_01
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Jenkins Secondary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Jenkins id=283 · jenkins

Aliases — catalog

  • Jenkins (CANONICAL) primary

Context tags (catalog)

Blue Ocean CI/CD Declarative Pipeline Docker Groovy Jenkinsfile Kubernetes agents artifact repository artifacts automation build triggers integration multibranch pipeline pipeline plugins shared libraries stages test automation version control webhooks

Stored enrichment (catalog DB)

Category
Tool
Sub-category
Ci Cd Tool
Vendor
CloudBees
License
mit
Year introduced
2011
Confidence
0.99
Version strategy
NOT_APPLICABLE

Maturity reasoning: Jenkins remains a common CI/CD requirement in job postings and enterprise DevOps stacks, with broad plugin ecosystem and long-running GitHub activity despite newer alternatives.

Skill profile (library / DB)

Skill nature
TOOL
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
13
Sub-category id
184
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • CI/CD Pipeline Platforms Catalog dimension db id 150

    Library dimension (catalog)

    Roles linked in library: DevOps Engineer

  • CI/CD for Machine Learning Catalog dimension db id 56

    Library dimension (catalog)

    Roles linked in library: ML Engineer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
CI/CD Pipeline Platforms
ci-cd-pipeline-platforms
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
CI/CD for Machine Learning
ci-cd-for-machine-learning
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Apache Subversion Secondary New / orchestrated API 3: new canonical path (new) New / unmatched skill (orchestrated in API 2)

Skill enrichment (orchestrator / LLM)

No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).

Derived legacy fields
Category
Version Control Tools
Sub-category
general
Skill nature
TOOL
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
MapReduce Primary New / orchestrated API 3: new canonical path (new) New / unmatched skill (orchestrated in API 2)

Skill enrichment (orchestrator / LLM)

No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).

Derived legacy fields
Category
Concepts
Sub-category
general
Skill nature
CONCEPT
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Spark Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Apache Spark id=1350 · apache-spark

Aliases — catalog

  • Apache Spark (CANONICAL)
  • apache spark 3 (VERSION)
  • spark (VERSION)
  • spark 3 (VERSION)
  • spark 3.x (VERSION)
  • spark3 (VERSION)

Context tags (catalog)

Apache Kafka Cluster Manager DAGScheduler Data Lake DataFrame ETL Hadoop MLlib Machine Learning PySpark RDD Scala Spark SQL Spark Streaming SparkSession

Stored enrichment (catalog DB)

Category
Framework
Sub-category
Distributed Data Processing Framework
Vendor
Apache Software Foundation
License
apache_2
Year introduced
2010
Confidence
0.94
Version strategy
SEPARATE_ENTITY
Version tag
3.x

Maturity reasoning: Apache Spark appears in many data engineering JDs and remains a standard for distributed ETL/ELT; its GitHub and vendor ecosystem activity stay strong, with Databricks and cloud platforms still promoting it.

Skill profile (library / DB)

Skill nature
FRAMEWORK
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
5
Sub-category id
1021
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • ETL and ELT Tooling Catalog dimension db id 24

    Library dimension (catalog)

    Roles linked in library: Data Engineer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
ETL and ELT Tooling
etl-and-elt-tooling
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
SQL Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: SQL id=101 · sql

Aliases — catalog

  • SQL (CANONICAL) primary

Context tags (catalog)

ACID CTE DDL DML ETL JOIN MySQL NoSQL OLAP ORM PostgreSQL SQL injection SQLite T-SQL data modeling data warehousing database normalization execution plan indexing joins normalization query optimization stored procedures subquery transaction isolation transaction management window functions

Stored enrichment (catalog DB)

Category
Language
Sub-category
Query Language
Vendor
ANSI
License
unknown
Year introduced
1974
Confidence
0.99
Version strategy
NOT_APPLICABLE

Maturity reasoning: SQL appears in a large share of data, backend, and analytics job descriptions and remains the default query language for PostgreSQL, MySQL, and cloud warehouses like Snowflake/BigQuery.

Skill profile (library / DB)

Skill nature
LANGUAGE
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
6
Sub-category id
97
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • Pega Programming Languages & DSLs Catalog dimension db id 267

    Library dimension (catalog)

    Roles linked in library: Pega Developer

  • Programming Languages for Data Work Catalog dimension db id 21

    Library dimension (catalog)

    Roles linked in library: Data Engineer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Pega Programming Languages & DSLs
pega-programming-languages-dsls
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Programming Languages for Data Work
programming-languages-for-data-work
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
ETL Primary New / orchestrated API 3: new canonical path (new) New / unmatched skill (orchestrated in API 2)

Skill enrichment (orchestrator / LLM)

No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).

Derived legacy fields
Category
Data Engineering Tools
Sub-category
general
Skill nature
PRACTICE
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
ELT Primary New / orchestrated API 3: new canonical path (new) New / unmatched skill (orchestrated in API 2)

Skill enrichment (orchestrator / LLM)

No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).

Derived legacy fields
Category
Data Engineering Tools
Sub-category
general
Skill nature
PRACTICE
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Data Warehousing Primary New / orchestrated API 3: new canonical path (new) New / unmatched skill (orchestrated in API 2)

Skill enrichment (orchestrator / LLM)

No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).

Derived legacy fields
Category
Data Engineering Tools
Sub-category
general
Skill nature
CONCEPT
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Data Modeling Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: domain modeling id=2379 · domain-modeling

Aliases — catalog

  • domain modeling (CANONICAL) primary
  • Domain Modeling (CANONICAL)

Context tags (catalog)

CQRS DDD ERD UML aggregate bounded context business logic context map context mapping data modeling domain events domain-driven design entities entity event sourcing event storming microservices repositories repository pattern service layer services value object value objects

Stored enrichment (catalog DB)

Category
Methodology
Sub-category
Domain Modeling
Confidence
0.90
Version strategy
NOT_APPLICABLE

Maturity reasoning: Common in software JDs under DDD/business analysis; many roles ask for domain modeling or domain-driven design, and it remains a standard design skill rather than a niche tool.

Skill profile (library / DB)

Skill nature
METHODOLOGY
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
8
Sub-category id
2831
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • Application Architecture Patterns Catalog dimension db id 293

    Library dimension (catalog)

    Roles linked in library: .NET Backend Developer, Python Backend Developer

  • Service Architecture and Design Patterns Catalog dimension db id 18

    Library dimension (catalog)

    Roles linked in library: Backend Developer, Java Backend Developer, Kotlin Backend Developer, Node.js Backend Developer, PHP Backend Developer, Ruby Backend Developer, Scala Backend Developer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Application Architecture Patterns
application-architecture-patterns
Skipped — no persistable v3 meta for new skill
skill_not_in_db_v3_proposed
Service Architecture and Design Patterns
service-architecture-and-design-patterns
Skipped — no persistable v3 meta for new skill
skill_not_in_db_v3_proposed
Python Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Python id=5 · python

Aliases — catalog

  • Python (CANONICAL) primary
  • Python 2 (VERSION)
  • Python 2.x (VERSION)
  • Python 3 (VERSION)
  • Python 3.10 (VERSION)
  • Python 3.11 (VERSION)
  • Python 3.12 (VERSION)
  • Python 3.x (VERSION)
  • py (VERSION)
  • py2 (VERSION)
  • py3 (VERSION)
  • python 3 (VERSION)
  • python 3.x (VERSION)
  • python2 (VERSION)
  • python3 (VERSION)
  • python3.x (VERSION)

Context tags (catalog)

API Django FastAPI Flask Jupyter NumPy PEP 8 Pandas REST SQLAlchemy asyncio pandas pip pytest type hints venv virtualenv

Stored enrichment (catalog DB)

Category
Language
Sub-category
Programming Language
Vendor
PSF
License
mit
Year introduced
1991
Confidence
0.99
Version strategy
SEPARATE_ENTITY
Version tag
3

Maturity reasoning: Python appears in a very high volume of job descriptions across data, backend, automation, and ML roles, and remains a default hiring-pipeline language on major job boards and tech stacks.

Skill profile (library / DB)

Skill nature
LANGUAGE
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
6
Sub-category id
96
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • Cloud Security Scripting & DSL Languages Catalog dimension db id 248

    Library dimension (catalog)

    Roles linked in library: Cloud Security Engineer

  • Programming Languages Catalog dimension db id 1

    Library dimension (catalog)

    Roles linked in library: Backend Developer, Fullstack Developer, Fullstack Developer

  • Programming Languages and Scripting Catalog dimension db id 59

    Library dimension (catalog)

    Roles linked in library: Cyber Security Engineer

  • Programming Languages for Data Work Catalog dimension db id 21

    Library dimension (catalog)

    Roles linked in library: Data Engineer

  • Programming Languages for ML Systems Catalog dimension db id 39

    Library dimension (catalog)

    Roles linked in library: ML Engineer, MLOps Engineer

  • Programming Languages for XR Catalog dimension db id 97

    Library dimension (catalog)

    Roles linked in library: AR/VR Engineer

  • Python Programming Catalog dimension db id 290

    Library dimension (catalog)

    Roles linked in library: Python Backend Developer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Cloud Security Scripting & DSL Languages
cloud-security-scripting-dsl-languages
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Programming Languages
programming-languages
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Programming Languages and Scripting
programming-languages-and-scripting
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Programming Languages for Data Work
programming-languages-for-data-work
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Programming Languages for ML Systems
programming-languages-for-ml-systems
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Programming Languages for XR
programming-languages-for-xr
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Python Programming
python-programming
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Java Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Java id=1 · java

Aliases — catalog

  • Java (CANONICAL) primary
  • JDK (VERSION)
  • JDK 10 (VERSION)
  • JDK 11 (VERSION)
  • JDK 12 (VERSION)
  • JDK 13 (VERSION)
  • JDK 14 (VERSION)
  • JDK 15 (VERSION)
  • JDK 16 (VERSION)
  • JDK 17 (VERSION)
  • JDK 18 (VERSION)
  • JDK 19 (VERSION)
  • JDK 20 (VERSION)
  • JDK 21 (VERSION)
  • JDK 5 (VERSION)
  • JDK 6 (VERSION)
  • JDK 7 (VERSION)
  • JDK 8 (VERSION)
  • JDK 9 (VERSION)
  • Java 1.0 (VERSION)
  • Java 1.1 (VERSION)
  • Java 1.2 (VERSION)
  • Java 1.3 (VERSION)
  • Java 1.4 (VERSION)
  • Java 1.5 (VERSION)
  • Java 1.6 (VERSION)
  • Java 1.7 (VERSION)
  • Java 1.8 (VERSION)
  • Java 10 (VERSION)
  • Java 11 (VERSION)
  • Java 12 (VERSION)
  • Java 13 (VERSION)
  • Java 14 (VERSION)
  • Java 15 (VERSION)
  • Java 16 (VERSION)
  • Java 17 (VERSION)
  • Java 18 (VERSION)
  • Java 19 (VERSION)
  • Java 20 (VERSION)
  • Java 21 (VERSION)
  • Java 5 (VERSION)
  • Java 6 (VERSION)
  • Java 7 (VERSION)
  • Java 8 (VERSION)
  • Java 9 (VERSION)
  • Java11 (VERSION)
  • Java17 (VERSION)
  • Java21 (VERSION)
  • Java8 (VERSION)
  • OpenJDK 11 (VERSION)
  • OpenJDK 17 (VERSION)
  • OpenJDK 21 (VERSION)
  • OpenJDK 8 (VERSION)
  • java 11 (VERSION)
  • java 17 (VERSION)
  • java 21 (VERSION)
  • java 4 (VERSION)
  • java 5 (VERSION)
  • java 6 (VERSION)
  • java 7 (VERSION)
  • java 8 (VERSION)
  • java lts (VERSION)
  • java-11 (VERSION)
  • java-17 (VERSION)
  • java-21 (VERSION)
  • java-4 (VERSION)
  • java-5 (VERSION)
  • java-6 (VERSION)
  • java-7 (VERSION)
  • java-8 (VERSION)
  • java11 (VERSION)
  • java17 (VERSION)
  • java21 (VERSION)
  • java4 (VERSION)
  • java5 (VERSION)
  • java6 (VERSION)
  • java7 (VERSION)
  • java8 (VERSION)
  • jdk 11 (VERSION)
  • jdk 17 (VERSION)
  • jdk 21 (VERSION)
  • jdk 4 (VERSION)
  • jdk 5 (VERSION)
  • jdk 6 (VERSION)
  • jdk 7 (VERSION)
  • jdk 8 (VERSION)
  • jdk11 (VERSION)
  • jdk17 (VERSION)
  • jdk21 (VERSION)
  • jdk4 (VERSION)
  • jdk5 (VERSION)
  • jdk6 (VERSION)
  • jdk7 (VERSION)
  • jdk8 (VERSION)
  • jvm21 (VERSION)

Context tags (catalog)

APIs Apache Tomcat Concurrency Design patterns Garbage collection GraalVM Gradle Hibernate JDBC JDK JPA JUnit JVM Java 8 Java EE JavaFX Kafka Lambda expressions Maven Microservices Mockito Object-oriented REST RESTful SOAP Servlets Spring Spring Boot Tomcat microservices

Stored enrichment (catalog DB)

Category
Language
Sub-category
Programming Language
Vendor
Oracle
License
other_open
Year introduced
1995
Confidence
0.99
Version strategy
SEPARATE_ENTITY
Version tag
21

Maturity reasoning: Java is a hiring-pipeline staple with very high JD volume across enterprise backend, Android, and cloud roles; it remains widely supported by major vendors and frameworks like Spring.

Skill profile (library / DB)

Skill nature
LANGUAGE
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
6
Sub-category id
96
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • Java Language and JVM Catalog dimension db id 279

    Library dimension (catalog)

    Roles linked in library: Java Backend Developer, Kotlin Backend Developer, Scala Backend Developer

  • Kotlin and Java Catalog dimension db id 161

    Library dimension (catalog)

    Roles linked in library: Android Developer

  • Native Mobile Languages Catalog dimension db id 274

    Library dimension (catalog)

    Roles linked in library: Native Mobile Developer

  • Pega Programming Languages & DSLs Catalog dimension db id 267

    Library dimension (catalog)

    Roles linked in library: Pega Developer

  • Programming Languages Catalog dimension db id 1

    Library dimension (catalog)

    Roles linked in library: Backend Developer, Fullstack Developer, Fullstack Developer

  • Programming Languages for Data Work Catalog dimension db id 21

    Library dimension (catalog)

    Roles linked in library: Data Engineer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Java Language and JVM
java-language-and-jvm
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Kotlin and Java
kotlin-and-java
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Native Mobile Languages
native-mobile-languages
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Pega Programming Languages & DSLs
pega-programming-languages-dsls
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Programming Languages
programming-languages
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Programming Languages for Data Work
programming-languages-for-data-work
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Hadoop Secondary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Hadoop id=1351 · hadoop

Aliases — catalog

  • Hadoop (CANONICAL)

Context tags (catalog)

Big Data Data Lake Distributed Computing ELT ETL Flume HDFS Hive Kafka MapReduce NoSQL Oozie Pig Spark Sqoop YARN

Stored enrichment (catalog DB)

Category
Framework
Sub-category
Data Processing Framework
Vendor
Apache Software Foundation
License
apache_2
Year introduced
2006
Confidence
0.90
Version strategy
NOT_APPLICABLE

Maturity reasoning: Job postings still mention Hadoop for legacy big-data stacks, but JD volume has fallen as Spark and cloud warehouses replaced MapReduce-era clusters.

Skill profile (library / DB)

Skill nature
FRAMEWORK
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
5
Sub-category id
91
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • ETL and ELT Tooling Catalog dimension db id 24

    Library dimension (catalog)

    Roles linked in library: Data Engineer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
ETL and ELT Tooling
etl-and-elt-tooling
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Hive Secondary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Hive id=2754 · hive

Aliases — catalog

  • Hive (CANONICAL) primary

Context tags (catalog)

Apache Apache Hive Bucketing ETL HQL Hive Metastore Hive SerDe HiveQL MapReduce SQL SQL-on-Hadoop big data bucketing columnar storage data lakes data warehousing integration metadata partitioning schema evolution

Stored enrichment (catalog DB)

Category
Datastore
Sub-category
Local Key Value Store
Vendor
Apache Software Foundation
License
apache_2
Year introduced
2010
Confidence
0.90
Version strategy
NOT_APPLICABLE

Maturity reasoning: Hive appears in Flutter/mobile JDs and package docs, but JD volume is far below SQLite/Realm and it’s mainly used for local key-value storage in Flutter apps.

Skill profile (library / DB)

Skill nature
TOOL
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
3
Sub-category id
2242
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • Local Persistence and Offline Behavior Catalog dimension db id 85

    Library dimension (catalog)

    Roles linked in library: Android Developer, Flutter Developer, Hybrid Mobile Developer, Native Mobile Developer, React Native Developer, iOS Developer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Local Persistence and Offline Behavior
local-persistence-and-offline-behavior
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Microsoft Azure Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Microsoft Azure id=97 · microsoft-azure

Aliases — catalog

  • Microsoft Azure (CANONICAL) primary

Context tags (catalog)

AKS ARM templates App Service Azure Active Directory Azure App Service Azure Blob Storage Azure Cosmos DB Azure DevOps Azure Functions Azure Kubernetes Service Azure Logic Apps Azure Monitor Azure Resource Manager Azure SQL Azure Virtual Machines Bicep Cloud Services Entra ID Functions IaaS Infrastructure as Code Key Vault Logic Apps PaaS Resource Group Serverless Computing Service Bus Storage Account Virtual Machines cloud migration microservices serverless

Stored enrichment (catalog DB)

Category
Platform
Sub-category
Cloud Platform
Vendor
Microsoft
License
other_open
Year introduced
2010
Confidence
0.99
Version strategy
NOT_APPLICABLE

Maturity reasoning: Azure appears in large volumes of cloud/DevOps job descriptions and is a core hyperscaler alongside AWS/GCP; Microsoft’s continued product investment and broad enterprise adoption signal mainstream demand.

Skill profile (library / DB)

Skill nature
PLATFORM
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
9
Sub-category id
46
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • Cloud & Hosting Providers Catalog dimension db id 414

    Library dimension (catalog)

    Roles linked in library: PHP Backend Developer

  • Cloud Platforms Catalog dimension db id 20

    Library dimension (catalog)

    Roles linked in library: .NET Backend Developer, Backend Developer, Cyber Security Engineer, Data Engineer, DevOps Engineer, Fullstack Developer, Go Backend Developer, Java Backend Developer, Kotlin Backend Developer, ML Engineer, MLOps Engineer, Node.js Backend Developer, Python Backend Developer, Scala Backend Developer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Cloud & Hosting Providers
cloud-hosting-providers
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Cloud Platforms
cloud-platforms
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Azure Stack Secondary New / orchestrated API 3: new canonical path (new) New / unmatched skill (orchestrated in API 2)

Skill enrichment (orchestrator / LLM)

No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).

Derived legacy fields
Category
Cloud Platforms
Sub-category
general
Skill nature
PLATFORM
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Databricks Secondary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Databricks id=1202 · databricks

Aliases — catalog

  • Databricks (CANONICAL)

Context tags (catalog)

Apache Spark Databricks Runtime Delta Lake MLflow SQL Analytics Spark cloud integration collaborative workspace data engineering data lakes data pipelines data visualization job scheduling machine learning notebooks real-time analytics

Stored enrichment (catalog DB)

Category
Platform
Sub-category
Data Analytics Platform
Vendor
Databricks, Inc.
License
other_open
Year introduced
2013
Confidence
0.97
Version strategy
NOT_APPLICABLE

Maturity reasoning: Databricks appears frequently in data engineering and analytics job postings, especially alongside Spark, Delta Lake, and lakehouse stacks; strong vendor adoption and broad enterprise usage signal mainstream demand.

Skill profile (library / DB)

Skill nature
PLATFORM
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
9
Sub-category id
911
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • React Frontend Development Catalog dimension db id 96

    Library dimension (catalog)

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
React Frontend Development
d_init_01
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
ADF Secondary New / orchestrated API 3: new canonical path (new) New / unmatched skill (orchestrated in API 2)

Skill enrichment (orchestrator / LLM)

No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).

Derived legacy fields
Category
Data Engineering Tools
Sub-category
general
Skill nature
PLATFORM
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
ADLS Secondary New / orchestrated API 3: new canonical path (new) New / unmatched skill (orchestrated in API 2)

Skill enrichment (orchestrator / LLM)

No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).

Derived legacy fields
Category
Data Engineering Tools
Sub-category
general
Skill nature
TOOL
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
C# Secondary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: C# id=4 · c

Aliases — catalog

  • C# (CANONICAL) primary
  • C (CANONICAL)
  • C# 1 (VERSION)
  • C# 10 (VERSION)
  • C# 11 (VERSION)
  • C# 12 (VERSION)
  • C# 13 (VERSION)
  • C# 14 (VERSION)
  • C# 2 (VERSION)
  • C# 3 (VERSION)
  • C# 4 (VERSION)
  • C# 5 (VERSION)
  • C# 6 (VERSION)
  • C# 7 (VERSION)
  • C# 8 (VERSION)
  • C# 9 (VERSION)
  • C# latest (VERSION)
  • C#1 (VERSION)
  • C#10 (VERSION)
  • C#11 (VERSION)
  • C#12 (VERSION)
  • C#2 (VERSION)
  • C#3 (VERSION)
  • C#4 (VERSION)
  • C#5 (VERSION)
  • C#6 (VERSION)
  • C#7 (VERSION)
  • C#8 (VERSION)
  • C#9 (VERSION)
  • C++ (CANONICAL)
  • C++03 (VERSION)
  • C++11 (VERSION)
  • C++14 (VERSION)
  • C++17 (VERSION)
  • C++20 (VERSION)
  • C++23 (VERSION)
  • C++26 (VERSION)
  • C++98 (VERSION)
  • c sharp (VERSION)
  • c# (VERSION)
  • cpp03 (VERSION)
  • cpp11 (VERSION)
  • cpp14 (VERSION)
  • cpp17 (VERSION)
  • cpp20 (VERSION)
  • cpp23 (VERSION)
  • cpp26 (VERSION)
  • cpp98 (VERSION)
  • csharp (VERSION)
  • modern C++ (VERSION)

Context tags (catalog)

.NET ASP.NET Azure Boost C# 9 C11 C99 CLR CMake Entity Framework LINQ MSBuild MVC Makefile NuGet Qt RAII Roslyn STL TDD Unity Visual Studio WPF Web API WinForms Xamarin async/await attributes buffer overflow constexpr delegates dependency injection dynamic memory events free function pointer gcc gdb header files inline interfaces linker malloc memory leak memory management move semantics multithreading operator overloading pointer pointers preprocessor segmentation fault smart pointers stdio.h stdlib.h struct syntax error templates typedef xUnit

Stored enrichment (catalog DB)

Category
Language
Sub-category
Programming Language
Vendor
Microsoft
License
mit
Year introduced
2000
Confidence
0.99
Version strategy
SEPARATE_ENTITY
Version tag
latest

Maturity reasoning: C# is a mainstream hiring staple with high JD volume across .NET, Azure, and enterprise roles; Microsoft continues active platform investment in .NET, reinforcing broad adoption.

Skill profile (library / DB)

Skill nature
LANGUAGE
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
6
Sub-category id
96
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • C# and .NET Languages Catalog dimension db id 362

    Library dimension (catalog)

    Roles linked in library: .NET Backend Developer

  • Cross-Platform App Languages Catalog dimension db id 167

    Library dimension (catalog)

    Roles linked in library: Hybrid Mobile Developer

  • Programming Languages Catalog dimension db id 1

    Library dimension (catalog)

    Roles linked in library: Backend Developer, Fullstack Developer, Fullstack Developer

  • Programming Languages for ML Systems Catalog dimension db id 39

    Library dimension (catalog)

    Roles linked in library: ML Engineer, MLOps Engineer

  • Programming Languages for XR Catalog dimension db id 97

    Library dimension (catalog)

    Roles linked in library: AR/VR Engineer

  • React Frontend Development Catalog dimension db id 96

    Library dimension (catalog)

  • Sitecore Development Languages Catalog dimension db id 438

    Library dimension (catalog)

    Roles linked in library: Sitecore Dev

  • Video Codec Languages and DSLs Catalog dimension db id 225

    Library dimension (catalog)

    Roles linked in library: Video Codec Engineer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
C# and .NET Languages
c-and-net-languages
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Cross-Platform App Languages
cross-platform-app-languages
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Programming Languages
programming-languages
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Programming Languages for ML Systems
programming-languages-for-ml-systems
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Programming Languages for XR
programming-languages-for-xr
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
React Frontend Development
d_init_01
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Sitecore Development Languages
sitecore-development-languages
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Video Codec Languages and DSLs
video-codec-languages-and-dsls
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
.NET Secondary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: .NET id=3237 · net

Aliases — catalog

  • .NET (CANONICAL) primary
  • .NET 5 (VERSION)
  • .NET 6 (VERSION)
  • .NET 7 (VERSION)
  • .NET 8 (VERSION)
  • .NET Core (VERSION)

Context tags (catalog)

ADO.NET ASP.NET Azure Blazor C# CLR Entity Framework LINQ MVC Microservices NuGet Visual Studio WPF Web API XAML

Stored enrichment (catalog DB)

Category
Runtime
Sub-category
Runtime Platform
Vendor
Microsoft
License
mit
Year introduced
2002
Confidence
0.90
Version strategy
SEPARATE_ENTITY
Version tag
.NET (modern, i.e., .NET 6+)

Maturity reasoning: .NET appears in large volumes of job postings across enterprise, cloud, and backend roles, and Microsoft continues active releases and LTS support, indicating broad hiring-pipeline adoption.

Skill profile (library / DB)

Skill nature
RUNTIME
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
67
Sub-category id
2564
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • C# and .NET Languages Catalog dimension db id 362

    Library dimension (catalog)

    Roles linked in library: .NET Backend Developer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
C# and .NET Languages
c-and-net-languages
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
StreamSets Secondary New / orchestrated API 3: new canonical path (new) New / unmatched skill (orchestrated in API 2)

Skill enrichment (orchestrator / LLM)

No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).

Derived legacy fields
Category
Data Engineering Tools
Sub-category
general
Skill nature
TOOL
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Snowflake Secondary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Snowflake id=105 · snowflake

Aliases — catalog

  • Snowflake (CANONICAL) primary

Context tags (catalog)

ELT ETL SQL Snowpark Snowpipe Streams Tasks Time Travel VARIANT data sharing data warehouse dbt semi-structured data virtual warehouse zero-copy cloning

Stored enrichment (catalog DB)

Category
Platform
Sub-category
Data Cloud Platform
Vendor
Snowflake Inc.
License
proprietary
Year introduced
2012
Confidence
0.98
Version strategy
NOT_APPLICABLE

Maturity reasoning: Snowflake appears frequently in data/analytics job postings and is a standard cloud data warehouse platform alongside BigQuery and Redshift.

Skill profile (library / DB)

Skill nature
PLATFORM
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
9
Sub-category id
113
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • Cloud Data Warehouses Catalog dimension db id 22

    Library dimension (catalog)

    Roles linked in library: Data Engineer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Cloud Data Warehouses
cloud-data-warehouses
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Tableau Secondary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Tableau id=150 · tableau

Aliases — catalog

  • Tableau (CANONICAL) primary

Context tags (catalog)

LOD expressions Tableau Cloud Tableau Desktop Tableau Prep Tableau Server actions calculated fields dashboards data blending data visualization extracts filters parameters published data sources workbooks

Stored enrichment (catalog DB)

Category
Platform
Sub-category
Bi Analytics Platform
Vendor
Tableau Software
License
proprietary
Year introduced
2003
Confidence
0.96
Version strategy
NOT_APPLICABLE

Maturity reasoning: Tableau appears frequently in BI/data analyst job descriptions and remains a standard enterprise analytics platform with strong vendor support and broad adoption.

Skill profile (library / DB)

Skill nature
PLATFORM
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
9
Sub-category id
111
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • BI and Visualization Tools Catalog dimension db id 31

    Library dimension (catalog)

    Roles linked in library: Data Engineer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
BI and Visualization Tools
bi-and-visualization-tools
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Alteryx Secondary New / orchestrated API 3: new canonical path (new) New / unmatched skill (orchestrated in API 2)

Skill enrichment (orchestrator / LLM)

No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).

Derived legacy fields
Category
Data Engineering Tools
Sub-category
general
Skill nature
TOOL
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED

All API 3 persistence rows

Same grid as the skill-extractor “Persistence items” table: one row per (skill × dimension) work item.

Skill Tag Dimension Skill↔dim Role↔dim Outcome Notes
AWS in_db
Cloud Platforms
cloud-platforms
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
AWS in_db
Cloud Platforms for AI Deployment
cloud-platforms-for-ai-deployment
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
AWS in_db
Cloud Provider Platforms
cloud-provider-platforms
Existing dimension (library) · Role↔dimension saved
AWS in_db
Cloud Security Posture Tools
cloud-security-posture-tools
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Kafka in_db
Asynchronous Messaging and Event Streaming
asynchronous-messaging-and-event-streaming
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Kafka in_db
Messaging and Background Jobs
messaging-and-background-jobs
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Kafka in_db
Messaging and Event Streaming
messaging-and-event-streaming
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Redshift in_db
Cloud Platforms
cloud-platforms
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
S3 in_db
Cloud Platforms
cloud-platforms
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
S3 in_db
React Frontend Development
d_init_01
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
S3 in_db
Systems Programming
d_init_02
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
DynamoDB new
NoSQL Databases
nosql-databases
Skipped — no persistable v3 meta for new skill skill_not_in_db_v3_proposed
Git in_db
React Frontend Development
d_init_01
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Jenkins in_db
CI/CD Pipeline Platforms
ci-cd-pipeline-platforms
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Jenkins in_db
CI/CD for Machine Learning
ci-cd-for-machine-learning
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Spark in_db
ETL and ELT Tooling
etl-and-elt-tooling
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
SQL in_db
Pega Programming Languages & DSLs
pega-programming-languages-dsls
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
SQL in_db
Programming Languages for Data Work
programming-languages-for-data-work
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Data Modeling new
Application Architecture Patterns
application-architecture-patterns
Skipped — no persistable v3 meta for new skill skill_not_in_db_v3_proposed
Data Modeling new
Service Architecture and Design Patterns
service-architecture-and-design-patterns
Skipped — no persistable v3 meta for new skill skill_not_in_db_v3_proposed
Python in_db
Cloud Security Scripting & DSL Languages
cloud-security-scripting-dsl-languages
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Python in_db
Programming Languages
programming-languages
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Python in_db
Programming Languages and Scripting
programming-languages-and-scripting
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Python in_db
Programming Languages for Data Work
programming-languages-for-data-work
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Python in_db
Programming Languages for ML Systems
programming-languages-for-ml-systems
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Python in_db
Programming Languages for XR
programming-languages-for-xr
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Python in_db
Python Programming
python-programming
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Java in_db
Java Language and JVM
java-language-and-jvm
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Java in_db
Kotlin and Java
kotlin-and-java
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Java in_db
Native Mobile Languages
native-mobile-languages
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Java in_db
Pega Programming Languages & DSLs
pega-programming-languages-dsls
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Java in_db
Programming Languages
programming-languages
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Java in_db
Programming Languages for Data Work
programming-languages-for-data-work
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Hadoop in_db
ETL and ELT Tooling
etl-and-elt-tooling
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Hive in_db
Local Persistence and Offline Behavior
local-persistence-and-offline-behavior
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Microsoft Azure in_db
Cloud & Hosting Providers
cloud-hosting-providers
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Microsoft Azure in_db
Cloud Platforms
cloud-platforms
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Databricks in_db
React Frontend Development
d_init_01
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
C# in_db
C# and .NET Languages
c-and-net-languages
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
C# in_db
Cross-Platform App Languages
cross-platform-app-languages
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
C# in_db
Programming Languages
programming-languages
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
C# in_db
Programming Languages for ML Systems
programming-languages-for-ml-systems
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
C# in_db
Programming Languages for XR
programming-languages-for-xr
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
C# in_db
React Frontend Development
d_init_01
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
C# in_db
Sitecore Development Languages
sitecore-development-languages
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
C# in_db
Video Codec Languages and DSLs
video-codec-languages-and-dsls
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
.NET in_db
C# and .NET Languages
c-and-net-languages
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Snowflake in_db
Cloud Data Warehouses
cloud-data-warehouses
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Tableau in_db
BI and Visualization Tools
bi-and-visualization-tools
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)

Library artifacts (this run)

Kind Detail DB id
canonical_skill_proposed Flume | type=Data Engineering Tools subtype=general nature=TOOL lifespan=MULTI_YEAR
canonical_skill_proposed Kinesis | type=Data Engineering Tools subtype=general nature=PLATFORM lifespan=MULTI_YEAR
canonical_skill_proposed EC2 | type=Cloud Platforms subtype=general nature=PLATFORM lifespan=MULTI_YEAR
canonical_skill_proposed Glue | type=Data Engineering Tools subtype=general nature=PLATFORM lifespan=MULTI_YEAR
canonical_skill_proposed EMR | type=Data Engineering Tools subtype=general nature=PLATFORM lifespan=MULTI_YEAR
canonical_skill_proposed Lambda | type=Cloud Platforms subtype=general nature=PLATFORM lifespan=MULTI_YEAR
canonical_skill_proposed QuickSight | type=Data Engineering Tools subtype=general nature=PLATFORM lifespan=MULTI_YEAR
canonical_skill_proposed Apache Subversion | type=Version Control Tools subtype=general nature=TOOL lifespan=MULTI_YEAR
canonical_skill_proposed MapReduce | type=Concepts subtype=general nature=CONCEPT lifespan=MULTI_YEAR
canonical_skill_proposed ETL | type=Data Engineering Tools subtype=general nature=PRACTICE lifespan=MULTI_YEAR
canonical_skill_proposed ELT | type=Data Engineering Tools subtype=general nature=PRACTICE lifespan=MULTI_YEAR
canonical_skill_proposed Data Warehousing | type=Data Engineering Tools subtype=general nature=CONCEPT lifespan=MULTI_YEAR
canonical_skill_proposed Azure Stack | type=Cloud Platforms subtype=general nature=PLATFORM lifespan=MULTI_YEAR
canonical_skill_proposed ADF | type=Data Engineering Tools subtype=general nature=PLATFORM lifespan=MULTI_YEAR
canonical_skill_proposed ADLS | type=Data Engineering Tools subtype=general nature=TOOL lifespan=MULTI_YEAR
canonical_skill_proposed StreamSets | type=Data Engineering Tools subtype=general nature=TOOL lifespan=MULTI_YEAR
canonical_skill_proposed Alteryx | type=Data Engineering Tools subtype=general nature=TOOL lifespan=MULTI_YEAR
dimension_skill_link_proposed DynamoDB ↔ NoSQL Databases
dimension_skill_link_proposed Data Modeling ↔ Application Architecture Patterns
dimension_skill_link_proposed Data Modeling ↔ Service Architecture and Design Patterns
nano JD Parser — gpt-4.1-nano click to toggle
RoleSenior – Cloud Experts
CompanyEY
ExperienceMinimum 10 years industry experience
DomainConsulting
JD type pass
Show raw JSON
{
  "JD_type": "pass",
  "about_company": {
    "source_marker": {
      "first_5_words": "EY exists to build a",
      "last_5_words": "facing our world today."
    },
    "text": "EY exists to build a better working world, helping to create long-term value for clients, people, and society and build trust in the capital markets.\nEnabled by data and technology, diverse EY teams in over 150 countries provide trust through assurance and help clients grow, transform, and operate.\nWorking across assurance, consulting, law, strategy, tax, and transactions, EY teams ask better questions to find new answers for the complex issues facing our world today.",
    "word_count": 64
  },
  "certifications": [],
  "company_name": "EY",
  "ctc": null,
  "domain": {
    "primary": {
      "aliases": [
        "Professional Services",
        "Advisory Services"
      ],
      "domain": "Consulting"
    },
    "secondary": null
  },
  "education": [
    {
      "level": "Bachelor\u0027s",
      "qualification": "BTECH/BE/MCA/MBA - null",
      "raw": "BE/BTech/MCA/MBA",
      "requirement": "required"
    }
  ],
  "experience": {
    "max": null,
    "min": 10,
    "raw": "Minimum 10 years industry experience"
  },
  "job_locations": [],
  "role": "Senior \u2013 Cloud Experts",
  "role_aliases": [
    "Cloud Expert",
    "Senior Cloud Consultant",
    "Cloud Solutions Architect"
  ],
  "role_archetype": "Data",
  "roles_and_responsibilities": [
    {
      "bullet_count": 0,
      "heading": "Your Key Responsibilities",
      "heading_was_present": true,
      "source_marker": {
        "first_5_words": "Your Key Responsibilities AWS Experience",
        "last_5_words": "new technologies and standards."
      },
      "text": "AWS\nExperience with Kafka, Flume and AWS tool stack such as Redshift and Kinesis are preferred.\nExperience building on AWS using S3, EC2, Redshift, Glue, EMR, DynamoDB, Lambda, QuickSight, etc.\nExperience using software version control tools (Git, Jenkins, Apache Subversion).\nAWS certifications or other related professional technical certifications.\nExperience with cloud or on-premise middleware and other enterprise integration technologies.\nExperience in writing MapReduce and/or Spark jobs.\nDemonstrated strength in architecting data warehouse solutions and integrating technical components.\nGood analytical skills with excellent knowledge of SQL.\n4+ years of work experience with very large data warehousing environment.\nExcellent communication skills, both written and verbal.\n7+ years of experience with detailed knowledge of data warehouse technical architectures, infrastructure components, ETL/ ELT and reporting/analytic tools.\n4+ years of experience data modelling concepts.\n3+ years of Python and/or Java development experience.\n3+ years\u2019 experience in Big Data stack environments (EMR, Hadoop, MapReduce, Hive).\n\nAzure\nIn-depth cloud domain experience with focus on Microsoft Azure / Microsoft Azure Stack solutions.\nExperience in writing MapReduce and/or Spark jobs.\nExperience building on Azure using Databricks, ADF/ADLS.\nSound programming, debugging/troubleshooting and problem-solving skills with familiarity in C# / .NET.\nGood to have experience in Streamsets, Snowflake, Kafka.\nFlexible and proactive/self-motivated working style with strong personal ownership of problem resolution.\nExcellent communicator (written and verbal formal and informal).\nAbility to multi-task under pressure and work independently with minimal supervision.\nStrong verbal and written communication skills.\nMust be a team player and enjoy working in a cooperative and collaborative team environment.\nAdaptable to new technologies and standards.",
      "word_count": 392
    },
    {
      "bullet_count": 0,
      "heading": "Skills And Attributes For Success",
      "heading_was_present": true,
      "source_marker": {
        "first_5_words": "Use an issue-based approach",
        "last_5_words": "like Tableau, Alteryx etc."
      },
      "text": "Use an issue-based approach to deliver growth, market and portfolio strategy engagements for corporates.\nStrong communication, presentation and team building skills and experience in producing high quality reports, papers, and presentations.\nExperience in executing and managing research and analysis of companies and markets, preferably from a commercial due diligence standpoint.\nExposure to tools like Tableau, Alteryx etc.",
      "word_count": 64
    },
    {
      "bullet_count": 0,
      "heading": "To qualify for the role, you must have",
      "heading_was_present": true,
      "source_marker": {
        "first_5_words": "To qualify for the role,",
        "last_5_words": "10 years industry experience."
      },
      "text": "BE/BTech/MCA/MBA\nMinimum 7 years hand-on experience in one or more key areas.\nMinimum 10 years industry experience.",
      "word_count": 24
    },
    {
      "bullet_count": 0,
      "heading": "What We Look For",
      "heading_was_present": true,
      "source_marker": {
        "first_5_words": "A Team of people with",
        "last_5_words": "businesses across a range of industries."
      },
      "text": "A Team of people with commercial acumen, technical experience and enthusiasm to learn new things in this fast-moving environment.\nAn opportunity to be a part of market-leading, multi-disciplinary team of 1400 + professionals, in the only integrated global transaction business worldwide.\nOpportunities to work with EY Consulting practices globally with leading businesses across a range of industries.",
      "word_count": 56
    }
  ],
  "urls": []
}
API 1 — extract-from-jd click to toggle
{
  "final_skills": [
    {
      "is_primary": true,
      "skill_name": "AWS"
    },
    {
      "is_primary": false,
      "skill_name": "Kafka"
    },
    {
      "is_primary": false,
      "skill_name": "Flume"
    },
    {
      "is_primary": false,
      "skill_name": "Redshift"
    },
    {
      "is_primary": false,
      "skill_name": "Kinesis"
    },
    {
      "is_primary": false,
      "skill_name": "S3"
    },
    {
      "is_primary": false,
      "skill_name": "EC2"
    },
    {
      "is_primary": false,
      "skill_name": "Glue"
    },
    {
      "is_primary": false,
      "skill_name": "EMR"
    },
    {
      "is_primary": false,
      "skill_name": "DynamoDB"
    },
    {
      "is_primary": false,
      "skill_name": "Lambda"
    },
    {
      "is_primary": false,
      "skill_name": "QuickSight"
    },
    {
      "is_primary": false,
      "skill_name": "Git"
    },
    {
      "is_primary": false,
      "skill_name": "Jenkins"
    },
    {
      "is_primary": false,
      "skill_name": "Apache Subversion"
    },
    {
      "is_primary": true,
      "skill_name": "MapReduce"
    },
    {
      "is_primary": true,
      "skill_name": "Spark"
    },
    {
      "is_primary": true,
      "skill_name": "SQL"
    },
    {
      "is_primary": true,
      "skill_name": "ETL"
    },
    {
      "is_primary": true,
      "skill_name": "ELT"
    },
    {
      "is_primary": true,
      "skill_name": "Data Warehousing"
    },
    {
      "is_primary": true,
      "skill_name": "Data Modeling"
    },
    {
      "is_primary": true,
      "skill_name": "Python"
    },
    {
      "is_primary": true,
      "skill_name": "Java"
    },
    {
      "is_primary": false,
      "skill_name": "Hadoop"
    },
    {
      "is_primary": false,
      "skill_name": "Hive"
    },
    {
      "is_primary": true,
      "skill_name": "Microsoft Azure"
    },
    {
      "is_primary": false,
      "skill_name": "Azure Stack"
    },
    {
      "is_primary": false,
      "skill_name": "Databricks"
    },
    {
      "is_primary": false,
      "skill_name": "ADF"
    },
    {
      "is_primary": false,
      "skill_name": "ADLS"
    },
    {
      "is_primary": false,
      "skill_name": "C#"
    },
    {
      "is_primary": false,
      "skill_name": ".NET"
    },
    {
      "is_primary": false,
      "skill_name": "StreamSets"
    },
    {
      "is_primary": false,
      "skill_name": "Snowflake"
    },
    {
      "is_primary": false,
      "skill_name": "Tableau"
    },
    {
      "is_primary": false,
      "skill_name": "Alteryx"
    }
  ],
  "jd_role": {
    "display_name": "Senior \u2013 Cloud Experts",
    "rationale": null,
    "role_aliases": [
      "Cloud Expert",
      "Senior Cloud Consultant",
      "Cloud Solutions Architect"
    ],
    "role_archetype": "Data",
    "slug": ""
  },
  "nano_parsed": {
    "JD_type": "pass",
    "about_company": {
      "source_marker": {
        "first_5_words": "EY exists to build a",
        "last_5_words": "facing our world today."
      },
      "text": "EY exists to build a better working world, helping to create long-term value for clients, people, and society and build trust in the capital markets.\nEnabled by data and technology, diverse EY teams in over 150 countries provide trust through assurance and help clients grow, transform, and operate.\nWorking across assurance, consulting, law, strategy, tax, and transactions, EY teams ask better questions to find new answers for the complex issues facing our world today.",
      "word_count": 64
    },
    "certifications": [],
    "company_name": "EY",
    "ctc": null,
    "domain": {
      "primary": {
        "aliases": [
          "Professional Services",
          "Advisory Services"
        ],
        "domain": "Consulting"
      },
      "secondary": null
    },
    "education": [
      {
        "level": "Bachelor\u0027s",
        "qualification": "BTECH/BE/MCA/MBA - null",
        "raw": "BE/BTech/MCA/MBA",
        "requirement": "required"
      }
    ],
    "experience": {
      "max": null,
      "min": 10,
      "raw": "Minimum 10 years industry experience"
    },
    "job_locations": [],
    "role": "Senior \u2013 Cloud Experts",
    "role_aliases": [
      "Cloud Expert",
      "Senior Cloud Consultant",
      "Cloud Solutions Architect"
    ],
    "role_archetype": "Data",
    "roles_and_responsibilities": [
      {
        "bullet_count": 0,
        "heading": "Your Key Responsibilities",
        "heading_was_present": true,
        "source_marker": {
          "first_5_words": "Your Key Responsibilities AWS Experience",
          "last_5_words": "new technologies and standards."
        },
        "text": "AWS\nExperience with Kafka, Flume and AWS tool stack such as Redshift and Kinesis are preferred.\nExperience building on AWS using S3, EC2, Redshift, Glue, EMR, DynamoDB, Lambda, QuickSight, etc.\nExperience using software version control tools (Git, Jenkins, Apache Subversion).\nAWS certifications or other related professional technical certifications.\nExperience with cloud or on-premise middleware and other enterprise integration technologies.\nExperience in writing MapReduce and/or Spark jobs.\nDemonstrated strength in architecting data warehouse solutions and integrating technical components.\nGood analytical skills with excellent knowledge of SQL.\n4+ years of work experience with very large data warehousing environment.\nExcellent communication skills, both written and verbal.\n7+ years of experience with detailed knowledge of data warehouse technical architectures, infrastructure components, ETL/ ELT and reporting/analytic tools.\n4+ years of experience data modelling concepts.\n3+ years of Python and/or Java development experience.\n3+ years\u2019 experience in Big Data stack environments (EMR, Hadoop, MapReduce, Hive).\n\nAzure\nIn-depth cloud domain experience with focus on Microsoft Azure / Microsoft Azure Stack solutions.\nExperience in writing MapReduce and/or Spark jobs.\nExperience building on Azure using Databricks, ADF/ADLS.\nSound programming, debugging/troubleshooting and problem-solving skills with familiarity in C# / .NET.\nGood to have experience in Streamsets, Snowflake, Kafka.\nFlexible and proactive/self-motivated working style with strong personal ownership of problem resolution.\nExcellent communicator (written and verbal formal and informal).\nAbility to multi-task under pressure and work independently with minimal supervision.\nStrong verbal and written communication skills.\nMust be a team player and enjoy working in a cooperative and collaborative team environment.\nAdaptable to new technologies and standards.",
        "word_count": 392
      },
      {
        "bullet_count": 0,
        "heading": "Skills And Attributes For Success",
        "heading_was_present": true,
        "source_marker": {
          "first_5_words": "Use an issue-based approach",
          "last_5_words": "like Tableau, Alteryx etc."
        },
        "text": "Use an issue-based approach to deliver growth, market and portfolio strategy engagements for corporates.\nStrong communication, presentation and team building skills and experience in producing high quality reports, papers, and presentations.\nExperience in executing and managing research and analysis of companies and markets, preferably from a commercial due diligence standpoint.\nExposure to tools like Tableau, Alteryx etc.",
        "word_count": 64
      },
      {
        "bullet_count": 0,
        "heading": "To qualify for the role, you must have",
        "heading_was_present": true,
        "source_marker": {
          "first_5_words": "To qualify for the role,",
          "last_5_words": "10 years industry experience."
        },
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}
API 2 — extract-details
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      "alias_persist_skipped_reason": "TODO: REMOVE AFTER TESTING \u2014 alias DB write disabled",
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        "rationale": "Models and manages data using non-relational database systems.",
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        "rationale": "Systems used to define, run, and maintain automated build and deployment workflows. This cluster is coherent because the role owns delivery automation end to end, including pipeline reliability and promotion logic.",
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          "rationale": null,
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          "rationale": null,
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        "rationale": "Proficiency in programming and domain-specific languages used to automate and script cloud security controls.",
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        "rationale": "Languages used to write security automation, analysis scripts, detection logic, and remediation helpers. This is the primary implementation surface for a cybersecurity engineer across tooling and response workflows.",
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        "source": "db"
      },
      "input_skill": "Python",
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      "roles_from_db": [
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          "rationale": null,
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      ]
    },
    {
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        "display_name": "Programming Languages for Data Work",
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        "rationale": "Languages used to implement data pipelines, transformations, and operational glue. This is the primary coding surface for building ingestion, enrichment, and automation logic in data engineering.",
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        "source": "db"
      },
      "input_skill": "Python",
      "llm_role": null,
      "roles_from_db": [
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          "display_name": "Data Engineer",
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          "rationale": null,
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    {
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        "rationale": "Languages used to build training code, inference services, evaluation jobs, and ML glue code. This is the primary implementation surface for ML engineers across experimentation and productionization.",
        "slug": "programming-languages-for-ml-systems",
        "source": "db"
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      "input_skill": "Python",
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      "roles_from_db": [
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          "rationale": null,
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        {
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          "rationale": null,
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    {
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        "display_name": "Programming Languages for XR",
        "id": 97,
        "rationale": "Primary implementation languages used to build immersive client features, interaction logic, and device-specific runtime behavior. This is the core coding surface for AR/VR experiences.",
        "slug": "programming-languages-for-xr",
        "source": "db"
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      "input_skill": "Python",
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      "roles_from_db": [
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          "rationale": null,
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    },
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        "id": 290,
        "rationale": "Core Python language skills used to implement backend business logic, request handlers, integrations, and service internals. This is the primary coding surface for the role.",
        "slug": "python-programming",
        "source": "db"
      },
      "input_skill": "Python",
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      "roles_from_db": [
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    {
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        "rationale": "Core Java implementation skills used to build backend service logic, utilities, and internal abstractions. This is the primary coding surface for the role and includes language features plus JVM behavior that affect correctness and maintainability.",
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      "roles_from_db": [
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          "display_name": "Java Backend Developer",
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          "rationale": null,
          "role_archetype": "Engineering",
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          "rationale": null,
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      ]
    },
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        "rationale": "Primary implementation languages for Android app features, platform integration, and client-side business logic. Android engineers use these languages to build screens, state flows, service adapters, and device-aware behavior.",
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      "input_skill": "Java",
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          "display_name": "Android Developer",
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          "rationale": null,
          "role_archetype": null,
          "slug": "android-engineer",
          "source": "db"
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      ]
    },
    {
      "dimension": {
        "difficulty_hint": "well_known",
        "display_name": "Native Mobile Languages",
        "id": 274,
        "rationale": "Primary implementation languages used to build platform-specific app features, UI logic, and device integrations. This is the core coding surface for native mobile work on one platform.",
        "slug": "native-mobile-languages",
        "source": "db"
      },
      "input_skill": "Java",
      "llm_role": null,
      "roles_from_db": [
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          "display_name": "Native Mobile Developer",
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      ]
    },
    {
      "dimension": {
        "difficulty_hint": "well_known",
        "display_name": "Pega Programming Languages \u0026 DSLs",
        "id": 267,
        "rationale": "Programming languages and domain-specific languages used in Pega development.",
        "slug": "pega-programming-languages-dsls",
        "source": "db"
      },
      "input_skill": "Java",
      "llm_role": null,
      "roles_from_db": [
        {
          "display_name": "Pega Developer",
          "id": 24,
          "rationale": null,
          "role_archetype": null,
          "slug": "pega-developer",
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      ]
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    {
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        "difficulty_hint": "well_known",
        "display_name": "Programming Languages",
        "id": 1,
        "rationale": "Primary implementation languages used to build client and server feature code. Full stack engineers need enough fluency to move across layers and implement product behavior end to end.",
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      "input_skill": "Java",
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      "roles_from_db": [
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          "display_name": "Backend Developer",
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          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "C#5",
          "alias_type": "VERSION",
          "id": 2232,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "C#6",
          "alias_type": "VERSION",
          "id": 2233,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "C#7",
          "alias_type": "VERSION",
          "id": 2234,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "C#8",
          "alias_type": "VERSION",
          "id": 2235,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "C#9",
          "alias_type": "VERSION",
          "id": 2236,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "C++",
          "alias_type": "CANONICAL",
          "id": 418,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "C++03",
          "alias_type": "VERSION",
          "id": 420,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "C++11",
          "alias_type": "VERSION",
          "id": 421,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "C++14",
          "alias_type": "VERSION",
          "id": 422,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "C++17",
          "alias_type": "VERSION",
          "id": 423,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "C++20",
          "alias_type": "VERSION",
          "id": 424,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "C++23",
          "alias_type": "VERSION",
          "id": 425,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "C++26",
          "alias_type": "VERSION",
          "id": 426,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "C++98",
          "alias_type": "VERSION",
          "id": 427,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "c sharp",
          "alias_type": "VERSION",
          "id": 4765,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "c#",
          "alias_type": "VERSION",
          "id": 4763,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "cpp03",
          "alias_type": "VERSION",
          "id": 879,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "cpp11",
          "alias_type": "VERSION",
          "id": 880,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "cpp14",
          "alias_type": "VERSION",
          "id": 881,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "cpp17",
          "alias_type": "VERSION",
          "id": 882,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "cpp20",
          "alias_type": "VERSION",
          "id": 883,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "cpp23",
          "alias_type": "VERSION",
          "id": 884,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "cpp26",
          "alias_type": "VERSION",
          "id": 885,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "cpp98",
          "alias_type": "VERSION",
          "id": 886,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "csharp",
          "alias_type": "VERSION",
          "id": 4764,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "modern C++",
          "alias_type": "VERSION",
          "id": 428,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        }
      ],
      "canonical": {
        "category_id": 6,
        "display_name": "C#",
        "id": 4,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "LANGUAGE",
        "slug": "c",
        "sub_category_id": 96,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "dimensions": [
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "C# and .NET Languages",
            "id": 362,
            "rationale": "Primary implementation languages and runtime features used to build backend service logic in the .NET ecosystem. This is the core coding surface for request handling, domain logic, and service internals.",
            "slug": "c-and-net-languages",
            "source": "db"
          },
          "input_skill": "C#",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": ".NET Backend Developer",
              "id": 83,
              "rationale": null,
              "role_archetype": "Engineering",
              "slug": "dotnet-backend-developer",
              "source": "db"
            }
          ]
        },
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "Cross-Platform App Languages",
            "id": 167,
            "rationale": "Languages used to implement shared mobile features across iOS and Android from a common codebase. This is the primary coding surface for hybrid app logic, UI behavior, and platform-specific branching.",
            "slug": "cross-platform-app-languages",
            "source": "db"
          },
          "input_skill": "C#",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": "Hybrid Mobile Developer",
              "id": 11,
              "rationale": null,
              "role_archetype": null,
              "slug": "hybrid-mobile-developer",
              "source": "db"
            }
          ]
        },
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "Programming Languages",
            "id": 1,
            "rationale": "Primary implementation languages used to build client and server feature code. Full stack engineers need enough fluency to move across layers and implement product behavior end to end.",
            "slug": "programming-languages",
            "source": "db"
          },
          "input_skill": "C#",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": "Backend Developer",
              "id": 1,
              "rationale": null,
              "role_archetype": "A Backend Engineer designs, builds, and maintains the server-side logic and data handling that power applications and services. They focus on implementing reliable business functionality, integrating with other systems, and ensuring the backend is scalable, maintainable, and observable.",
              "slug": "backend-engineer",
              "source": "db"
            },
            {
              "display_name": "Fullstack Developer",
              "id": 435,
              "rationale": null,
              "role_archetype": "Engineering",
              "slug": "fullstack-developer",
              "source": "db"
            },
            {
              "display_name": "Fullstack Developer",
              "id": 15,
              "rationale": null,
              "role_archetype": null,
              "slug": "full-stack-engineer",
              "source": "db"
            }
          ]
        },
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "Programming Languages for ML Systems",
            "id": 39,
            "rationale": "Languages used to build training code, inference services, evaluation jobs, and ML glue code. This is the primary implementation surface for ML engineers across experimentation and productionization.",
            "slug": "programming-languages-for-ml-systems",
            "source": "db"
          },
          "input_skill": "C#",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": "ML Engineer",
              "id": 3,
              "rationale": null,
              "role_archetype": null,
              "slug": "ml-engineer",
              "source": "db"
            },
            {
              "display_name": "MLOps Engineer",
              "id": 16,
              "rationale": null,
              "role_archetype": null,
              "slug": "ml-ops-engineer",
              "source": "db"
            }
          ]
        },
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "Programming Languages for XR",
            "id": 97,
            "rationale": "Primary implementation languages used to build immersive client features, interaction logic, and device-specific runtime behavior. This is the core coding surface for AR/VR experiences.",
            "slug": "programming-languages-for-xr",
            "source": "db"
          },
          "input_skill": "C#",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": "AR/VR Engineer",
              "id": 8,
              "rationale": null,
              "role_archetype": null,
              "slug": "ar-vr-engineer",
              "source": "db"
            }
          ]
        },
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "React Frontend Development",
            "id": 96,
            "rationale": "Building interactive web user interfaces with React.js, including component composition, state management, hooks, and rendering patterns. React.js belongs here because it is a core library for client-side UI development in modern web applications.",
            "slug": "d_init_01",
            "source": "db"
          },
          "input_skill": "C#",
          "llm_role": null,
          "roles_from_db": []
        },
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "Sitecore Development Languages",
            "id": 438,
            "rationale": "Core implementation languages and markup used to build Sitecore customizations, rendering logic, and site behavior. This is the primary authoring surface for Sitecore-specific code and templates.",
            "slug": "sitecore-development-languages",
            "source": "db"
          },
          "input_skill": "C#",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": "Sitecore Dev",
              "id": 233,
              "rationale": null,
              "role_archetype": "Engineering",
              "slug": "sitecore-dev",
              "source": "db"
            }
          ]
        },
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "Video Codec Languages and DSLs",
            "id": 225,
            "rationale": "Implementation languages and codec-specific description languages used to build compression, decoding, and transform logic. This is the primary coding surface for codec behavior and bitstream manipulation.",
            "slug": "video-codec-languages-and-dsls",
            "source": "db"
          },
          "input_skill": "C#",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": "Video Codec Engineer",
              "id": 22,
              "rationale": null,
              "role_archetype": null,
              "slug": "video-codec-engineer",
              "source": "db"
            }
          ]
        }
      ],
      "input_skill": "C#",
      "matched_via": "alias",
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": null,
      "source_tag": "db",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [
        {
          "alias_text": ".NET",
          "alias_type": "CANONICAL",
          "id": 4766,
          "is_primary": true,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": ".NET 5",
          "alias_type": "VERSION",
          "id": 4767,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": ".NET 6",
          "alias_type": "VERSION",
          "id": 4768,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": ".NET 7",
          "alias_type": "VERSION",
          "id": 4769,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": ".NET 8",
          "alias_type": "VERSION",
          "id": 4770,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": ".NET Core",
          "alias_type": "VERSION",
          "id": 4771,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        }
      ],
      "canonical": {
        "category_id": 67,
        "display_name": ".NET",
        "id": 3237,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "RUNTIME",
        "slug": "net",
        "sub_category_id": 2564,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "dimensions": [
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "C# and .NET Languages",
            "id": 362,
            "rationale": "Primary implementation languages and runtime features used to build backend service logic in the .NET ecosystem. This is the core coding surface for request handling, domain logic, and service internals.",
            "slug": "c-and-net-languages",
            "source": "db"
          },
          "input_skill": ".NET",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": ".NET Backend Developer",
              "id": 83,
              "rationale": null,
              "role_archetype": "Engineering",
              "slug": "dotnet-backend-developer",
              "source": "db"
            }
          ]
        }
      ],
      "input_skill": ".NET",
      "matched_via": "alias",
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": null,
      "source_tag": "db",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [],
      "canonical": null,
      "dimensions": [],
      "input_skill": "StreamSets",
      "matched_via": null,
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": {
        "derived": {
          "category": "Data Engineering Tools",
          "skill_nature": "TOOL",
          "sub_category": "general",
          "typical_lifespan": "MULTI_YEAR",
          "version_strategy": "UNVERSIONED",
          "volatility": "MEDIUM"
        },
        "enrichment": null,
        "keep_log": [],
        "locked_dimensions": [],
        "merge_log": [],
        "placed": null,
        "relationships": null,
        "skill_id": "streamsets",
        "split_log": [],
        "typed": null,
        "warnings": []
      },
      "source_tag": "llm",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [
        {
          "alias_text": "Snowflake",
          "alias_type": "CANONICAL",
          "id": 299,
          "is_primary": true,
          "match_strategy": "CASE_INSENSITIVE"
        }
      ],
      "canonical": {
        "category_id": 9,
        "display_name": "Snowflake",
        "id": 105,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "PLATFORM",
        "slug": "snowflake",
        "sub_category_id": 113,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "dimensions": [
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "Cloud Data Warehouses",
            "id": 22,
            "rationale": "Managed analytical storage and compute platforms used for curated datasets, reporting, and downstream analytics. These systems are central to data modeling, performance tuning, and cost-aware query design.",
            "slug": "cloud-data-warehouses",
            "source": "db"
          },
          "input_skill": "Snowflake",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": "Data Engineer",
              "id": 2,
              "rationale": null,
              "role_archetype": null,
              "slug": "data-engineer",
              "source": "db"
            }
          ]
        }
      ],
      "input_skill": "Snowflake",
      "matched_via": "alias",
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": null,
      "source_tag": "db",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [
        {
          "alias_text": "Tableau",
          "alias_type": "CANONICAL",
          "id": 359,
          "is_primary": true,
          "match_strategy": "CASE_INSENSITIVE"
        }
      ],
      "canonical": {
        "category_id": 9,
        "display_name": "Tableau",
        "id": 150,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "PLATFORM",
        "slug": "tableau",
        "sub_category_id": 111,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "dimensions": [
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "BI and Visualization Tools",
            "id": 31,
            "rationale": "Tools used to expose curated data to analysts and business users through dashboards, reports, and semantic exploration. Data engineers support these tools by shaping reliable datasets and performant models.",
            "slug": "bi-and-visualization-tools",
            "source": "db"
          },
          "input_skill": "Tableau",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": "Data Engineer",
              "id": 2,
              "rationale": null,
              "role_archetype": null,
              "slug": "data-engineer",
              "source": "db"
            }
          ]
        }
      ],
      "input_skill": "Tableau",
      "matched_via": "alias",
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": null,
      "source_tag": "db",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [],
      "canonical": null,
      "dimensions": [],
      "input_skill": "Alteryx",
      "matched_via": null,
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": {
        "derived": {
          "category": "Data Engineering Tools",
          "skill_nature": "TOOL",
          "sub_category": "general",
          "typical_lifespan": "MULTI_YEAR",
          "version_strategy": "UNVERSIONED",
          "volatility": "MEDIUM"
        },
        "enrichment": null,
        "keep_log": [],
        "locked_dimensions": [],
        "merge_log": [],
        "placed": null,
        "relationships": null,
        "skill_id": "alteryx",
        "split_log": [],
        "typed": null,
        "warnings": []
      },
      "source_tag": "llm",
      "was_in_llm_skills": true
    }
  ],
  "unmatched_skills": [
    "Flume",
    "Kinesis",
    "EC2",
    "Glue",
    "EMR",
    "Lambda",
    "QuickSight",
    "Apache Subversion",
    "MapReduce",
    "ETL",
    "ELT",
    "Data Warehousing",
    "Azure Stack",
    "ADF",
    "ADLS",
    "StreamSets",
    "Alteryx"
  ]
}
API 3 — final-role-output
{
  "chosen_role": {
    "display_name": "Cloud Architect",
    "id": 9,
    "rationale": "Domain=Cloud Engineering \u2192 sub-role multicloud-architect; The JD is centered on architecting data warehouse and integration solutions across AWS and Azure, which best matches a cloud architecture role rather than a single-platform admin or developer role.",
    "role_archetype": null,
    "slug": "cloud-architect",
    "source": "db"
  },
  "chosen_role_resolution": "in_db",
  "final_input_skills": [
    {
      "skill": "AWS",
      "tag": "in_db"
    },
    {
      "skill": "Kafka",
      "tag": "in_db"
    },
    {
      "skill": "Flume",
      "tag": "new"
    },
    {
      "skill": "Redshift",
      "tag": "in_db"
    },
    {
      "skill": "Kinesis",
      "tag": "new"
    },
    {
      "skill": "S3",
      "tag": "in_db"
    },
    {
      "skill": "EC2",
      "tag": "new"
    },
    {
      "skill": "Glue",
      "tag": "new"
    },
    {
      "skill": "EMR",
      "tag": "new"
    },
    {
      "skill": "DynamoDB",
      "tag": "in_db"
    },
    {
      "skill": "Lambda",
      "tag": "new"
    },
    {
      "skill": "QuickSight",
      "tag": "new"
    },
    {
      "skill": "Git",
      "tag": "in_db"
    },
    {
      "skill": "Jenkins",
      "tag": "in_db"
    },
    {
      "skill": "Apache Subversion",
      "tag": "new"
    },
    {
      "skill": "MapReduce",
      "tag": "new"
    },
    {
      "skill": "Spark",
      "tag": "in_db"
    },
    {
      "skill": "SQL",
      "tag": "in_db"
    },
    {
      "skill": "ETL",
      "tag": "new"
    },
    {
      "skill": "ELT",
      "tag": "new"
    },
    {
      "skill": "Data Warehousing",
      "tag": "new"
    },
    {
      "skill": "Data Modeling",
      "tag": "in_db"
    },
    {
      "skill": "Python",
      "tag": "in_db"
    },
    {
      "skill": "Java",
      "tag": "in_db"
    },
    {
      "skill": "Hadoop",
      "tag": "in_db"
    },
    {
      "skill": "Hive",
      "tag": "in_db"
    },
    {
      "skill": "Microsoft Azure",
      "tag": "in_db"
    },
    {
      "skill": "Azure Stack",
      "tag": "new"
    },
    {
      "skill": "Databricks",
      "tag": "in_db"
    },
    {
      "skill": "ADF",
      "tag": "new"
    },
    {
      "skill": "ADLS",
      "tag": "new"
    },
    {
      "skill": "C#",
      "tag": "in_db"
    },
    {
      "skill": ".NET",
      "tag": "in_db"
    },
    {
      "skill": "StreamSets",
      "tag": "new"
    },
    {
      "skill": "Snowflake",
      "tag": "in_db"
    },
    {
      "skill": "Tableau",
      "tag": "in_db"
    },
    {
      "skill": "Alteryx",
      "tag": "new"
    }
  ],
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        "skipped_reason": "skill_not_in_db_v3_proposed"
      },
      {
        "chosen_role_id": 9,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "React Frontend Development",
          "id": 96,
          "rationale": "Building interactive web user interfaces with React.js, including component composition, state management, hooks, and rendering patterns. React.js belongs here because it is a core library for client-side UI development in modern web applications.",
          "slug": "d_init_01",
          "source": "db"
        },
        "dimension_id": 96,
        "input_skill": "Git",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [],
        "skill_dimension_saved": true,
        "skill_id": 1002,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 9,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "CI/CD Pipeline Platforms",
          "id": 150,
          "rationale": "Systems used to define, run, and maintain automated build and deployment workflows. This cluster is coherent because the role owns delivery automation end to end, including pipeline reliability and promotion logic.",
          "slug": "ci-cd-pipeline-platforms",
          "source": "db"
        },
        "dimension_id": 150,
        "input_skill": "Jenkins",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [
          {
            "display_name": "DevOps Engineer",
            "id": 10,
            "rationale": null,
            "role_archetype": null,
            "slug": "devops-engineer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 283,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 9,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "CI/CD for Machine Learning",
          "id": 56,
          "rationale": "Tools and platforms for automating ML model integration, testing, and deployment pipelines.",
          "slug": "ci-cd-for-machine-learning",
          "source": "db"
        },
        "dimension_id": 56,
        "input_skill": "Jenkins",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [
          {
            "display_name": "ML Engineer",
            "id": 3,
            "rationale": null,
            "role_archetype": null,
            "slug": "ml-engineer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 283,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 9,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "ETL and ELT Tooling",
          "id": 24,
          "rationale": "Packaged tools for extracting, loading, and transforming data across systems. This dimension covers connector-based ingestion, transformation frameworks, and managed integration products.",
          "slug": "etl-and-elt-tooling",
          "source": "db"
        },
        "dimension_id": 24,
        "input_skill": "Spark",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [
          {
            "display_name": "Data Engineer",
            "id": 2,
            "rationale": null,
            "role_archetype": null,
            "slug": "data-engineer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 1350,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 9,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Pega Programming Languages \u0026 DSLs",
          "id": 267,
          "rationale": "Programming languages and domain-specific languages used in Pega development.",
          "slug": "pega-programming-languages-dsls",
          "source": "db"
        },
        "dimension_id": 267,
        "input_skill": "SQL",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [
          {
            "display_name": "Pega Developer",
            "id": 24,
            "rationale": null,
            "role_archetype": null,
            "slug": "pega-developer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 101,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 9,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Programming Languages for Data Work",
          "id": 21,
          "rationale": "Languages used to implement data pipelines, transformations, and operational glue. This is the primary coding surface for building ingestion, enrichment, and automation logic in data engineering.",
          "slug": "programming-languages-for-data-work",
          "source": "db"
        },
        "dimension_id": 21,
        "input_skill": "SQL",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [
          {
            "display_name": "Data Engineer",
            "id": 2,
            "rationale": null,
            "role_archetype": null,
            "slug": "data-engineer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 101,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 9,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Application Architecture Patterns",
          "id": 293,
          "rationale": "Structural patterns for organizing Python backend code into maintainable modules, layers, and feature boundaries. This is a coherent cluster because senior backend developers are expected to refactor and shape service internals over time.",
          "slug": "application-architecture-patterns",
          "source": "db"
        },
        "dimension_id": 293,
        "input_skill": "Data Modeling",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "Skipped \u2014 no persistable v3 meta for new skill",
        "role_dimension_saved": false,
        "roles_from_db": [
          {
            "display_name": ".NET Backend Developer",
            "id": 83,
            "rationale": null,
            "role_archetype": "Engineering",
            "slug": "dotnet-backend-developer",
            "source": "db"
          },
          {
            "display_name": "Python Backend Developer",
            "id": 80,
            "rationale": null,
            "role_archetype": "Engineering",
            "slug": "python-backend-developer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": false,
        "skill_id": null,
        "skill_tag": "new",
        "skipped_reason": "skill_not_in_db_v3_proposed"
      },
      {
        "chosen_role_id": 9,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Service Architecture and Design Patterns",
          "id": 18,
          "rationale": "Reusable backend design patterns used to structure service code and boundaries. Covers layering, dependency management, domain modeling, and maintainable service organization.",
          "slug": "service-architecture-and-design-patterns",
          "source": "db"
        },
        "dimension_id": 18,
        "input_skill": "Data Modeling",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "Skipped \u2014 no persistable v3 meta for new skill",
        "role_dimension_saved": false,
        "roles_from_db": [
          {
            "display_name": "Backend Developer",
            "id": 1,
            "rationale": null,
            "role_archetype": "A Backend Engineer designs, builds, and maintains the server-side logic and data handling that power applications and services. They focus on implementing reliable business functionality, integrating with other systems, and ensuring the backend is scalable, maintainable, and observable.",
            "slug": "backend-engineer",
            "source": "db"
          },
          {
            "display_name": "Java Backend Developer",
            "id": 79,
            "rationale": null,
            "role_archetype": "Engineering",
            "slug": "java-backend-developer",
            "source": "db"
          },
          {
            "display_name": "Kotlin Backend Developer",
            "id": 84,
            "rationale": null,
            "role_archetype": "Engineering",
            "slug": "kotlin-server-backend-developer",
            "source": "db"
          },
          {
            "display_name": "Node.js Backend Developer",
            "id": 82,
            "rationale": null,
            "role_archetype": "Engineering",
            "slug": "node-backend-developer",
            "source": "db"
          },
          {
            "display_name": "PHP Backend Developer",
            "id": 86,
            "rationale": null,
            "role_archetype": "Engineering",
            "slug": "php-backend-developer",
            "source": "db"
          },
          {
            "display_name": "Ruby Backend Developer",
            "id": 85,
            "rationale": null,
            "role_archetype": "Engineering",
            "slug": "ruby-backend-developer",
            "source": "db"
          },
          {
            "display_name": "Scala Backend Developer",
            "id": 87,
            "rationale": null,
            "role_archetype": "Engineering",
            "slug": "scala-backend-developer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": false,
        "skill_id": null,
        "skill_tag": "new",
        "skipped_reason": "skill_not_in_db_v3_proposed"
      },
      {
        "chosen_role_id": 9,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Cloud Security Scripting \u0026 DSL Languages",
          "id": 248,
          "rationale": "Proficiency in programming and domain-specific languages used to automate and script cloud security controls.",
          "slug": "cloud-security-scripting-dsl-languages",
          "source": "db"
        },
        "dimension_id": 248,
        "input_skill": "Python",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [
          {
            "display_name": "Cloud Security Engineer",
            "id": 23,
            "rationale": null,
            "role_archetype": null,
            "slug": "cloud-security-engineer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 5,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 9,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Programming Languages",
          "id": 1,
          "rationale": "Primary implementation languages used to build client and server feature code. Full stack engineers need enough fluency to move across layers and implement product behavior end to end.",
          "slug": "programming-languages",
          "source": "db"
        },
        "dimension_id": 1,
        "input_skill": "Python",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [
          {
            "display_name": "Backend Developer",
            "id": 1,
            "rationale": null,
            "role_archetype": "A Backend Engineer designs, builds, and maintains the server-side logic and data handling that power applications and services. They focus on implementing reliable business functionality, integrating with other systems, and ensuring the backend is scalable, maintainable, and observable.",
            "slug": "backend-engineer",
            "source": "db"
          },
          {
            "display_name": "Fullstack Developer",
            "id": 435,
            "rationale": null,
            "role_archetype": "Engineering",
            "slug": "fullstack-developer",
            "source": "db"
          },
          {
            "display_name": "Fullstack Developer",
            "id": 15,
            "rationale": null,
            "role_archetype": null,
            "slug": "full-stack-engineer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 5,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 9,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Programming Languages and Scripting",
          "id": 59,
          "rationale": "Languages used to write security automation, analysis scripts, detection logic, and remediation helpers. This is the primary implementation surface for a cybersecurity engineer across tooling and response workflows.",
          "slug": "programming-languages-and-scripting",
          "source": "db"
        },
        "dimension_id": 59,
        "input_skill": "Python",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [
          {
            "display_name": "Cyber Security Engineer",
            "id": 5,
            "rationale": null,
            "role_archetype": null,
            "slug": "cybersecurity-engineer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 5,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 9,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Programming Languages for Data Work",
          "id": 21,
          "rationale": "Languages used to implement data pipelines, transformations, and operational glue. This is the primary coding surface for building ingestion, enrichment, and automation logic in data engineering.",
          "slug": "programming-languages-for-data-work",
          "source": "db"
        },
        "dimension_id": 21,
        "input_skill": "Python",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [
          {
            "display_name": "Data Engineer",
            "id": 2,
            "rationale": null,
            "role_archetype": null,
            "slug": "data-engineer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 5,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 9,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Programming Languages for ML Systems",
          "id": 39,
          "rationale": "Languages used to build training code, inference services, evaluation jobs, and ML glue code. This is the primary implementation surface for ML engineers across experimentation and productionization.",
          "slug": "programming-languages-for-ml-systems",
          "source": "db"
        },
        "dimension_id": 39,
        "input_skill": "Python",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [
          {
            "display_name": "ML Engineer",
            "id": 3,
            "rationale": null,
            "role_archetype": null,
            "slug": "ml-engineer",
            "source": "db"
          },
          {
            "display_name": "MLOps Engineer",
            "id": 16,
            "rationale": null,
            "role_archetype": null,
            "slug": "ml-ops-engineer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 5,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 9,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Programming Languages for XR",
          "id": 97,
          "rationale": "Primary implementation languages used to build immersive client features, interaction logic, and device-specific runtime behavior. This is the core coding surface for AR/VR experiences.",
          "slug": "programming-languages-for-xr",
          "source": "db"
        },
        "dimension_id": 97,
        "input_skill": "Python",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [
          {
            "display_name": "AR/VR Engineer",
            "id": 8,
            "rationale": null,
            "role_archetype": null,
            "slug": "ar-vr-engineer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 5,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 9,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Python Programming",
          "id": 290,
          "rationale": "Core Python language skills used to implement backend business logic, request handlers, integrations, and service internals. This is the primary coding surface for the role.",
          "slug": "python-programming",
          "source": "db"
        },
        "dimension_id": 290,
        "input_skill": "Python",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [
          {
            "display_name": "Python Backend Developer",
            "id": 80,
            "rationale": null,
            "role_archetype": "Engineering",
            "slug": "python-backend-developer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 5,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 9,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Java Language and JVM",
          "id": 279,
          "rationale": "Core Java implementation skills used to build backend service logic, utilities, and internal abstractions. This is the primary coding surface for the role and includes language features plus JVM behavior that affect correctness and maintainability.",
          "slug": "java-language-and-jvm",
          "source": "db"
        },
        "dimension_id": 279,
        "input_skill": "Java",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [
          {
            "display_name": "Java Backend Developer",
            "id": 79,
            "rationale": null,
            "role_archetype": "Engineering",
            "slug": "java-backend-developer",
            "source": "db"
          },
          {
            "display_name": "Kotlin Backend Developer",
            "id": 84,
            "rationale": null,
            "role_archetype": "Engineering",
            "slug": "kotlin-server-backend-developer",
            "source": "db"
          },
          {
            "display_name": "Scala Backend Developer",
            "id": 87,
            "rationale": null,
            "role_archetype": "Engineering",
            "slug": "scala-backend-developer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 1,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 9,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Kotlin and Java",
          "id": 161,
          "rationale": "Primary implementation languages for Android app features, platform integration, and client-side business logic. Android engineers use these languages to build screens, state flows, service adapters, and device-aware behavior.",
          "slug": "kotlin-and-java",
          "source": "db"
        },
        "dimension_id": 161,
        "input_skill": "Java",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [
          {
            "display_name": "Android Developer",
            "id": 4,
            "rationale": null,
            "role_archetype": null,
            "slug": "android-engineer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 1,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 9,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Native Mobile Languages",
          "id": 274,
          "rationale": "Primary implementation languages used to build platform-specific app features, UI logic, and device integrations. This is the core coding surface for native mobile work on one platform.",
          "slug": "native-mobile-languages",
          "source": "db"
        },
        "dimension_id": 274,
        "input_skill": "Java",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [
          {
            "display_name": "Native Mobile Developer",
            "id": 75,
            "rationale": null,
            "role_archetype": "Engineering",
            "slug": "native-mobile-developer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 1,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 9,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Pega Programming Languages \u0026 DSLs",
          "id": 267,
          "rationale": "Programming languages and domain-specific languages used in Pega development.",
          "slug": "pega-programming-languages-dsls",
          "source": "db"
        },
        "dimension_id": 267,
        "input_skill": "Java",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [
          {
            "display_name": "Pega Developer",
            "id": 24,
            "rationale": null,
            "role_archetype": null,
            "slug": "pega-developer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 1,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 9,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Programming Languages",
          "id": 1,
          "rationale": "Primary implementation languages used to build client and server feature code. Full stack engineers need enough fluency to move across layers and implement product behavior end to end.",
          "slug": "programming-languages",
          "source": "db"
        },
        "dimension_id": 1,
        "input_skill": "Java",
        "llm_role": null,
        "matched_chosen_role": false,
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      }
    ],
    "new_skills_created": 0,
    "role_dimension_saved": 0,
    "skill_dimension_saved": 0,
    "skipped": 3
  },
  "planner_output": null,
  "run_id": "d865013f-3eab-45e7-8cf5-c743b92d0c2c"
}

LLM Calls

Every model call made for this run, in pipeline order. Click a card to see the model's response.

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